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Best GPU for AI Workstations in 2026: A Buyer's Guide
The right AI GPU depends on the model you're running, the form factor of the build, and whether the workload is single-user development or production serving. This guide walks through the current 2026 lineup (RTX PRO Blackwell, RTX 6000 Ada, L40S, H100, H200, B200), shows the spec differences that matter, and maps workloads to GPUs so the choice is grounded in the workload, not the marketing tier.
The five specs that actually matter for AI
Among the dozens of specs on a GPU datasheet, five drive AI performance:
- VRAM capacity. Determines what model size fits. A 96GB card runs 70B at Q4 with headroom; a 24GB card runs 13B at Q8 comfortably.
- Memory bandwidth. LLM inference is memory-bandwidth-bound. 8 TB/s on B200 versus 1.79 TB/s on RTX PRO 6000 Blackwell is roughly a 4.5x throughput difference at the bandwidth limit.
- Tensor Core throughput. Determines training and prefill speed. Generation matters: 5th-gen Tensor Cores on Blackwell add native FP4 support, which doubles inference throughput on supported workloads.
- ECC memory. Required for long fine-tuning runs and any production workload. All professional cards include it; gaming cards do not.
- Interconnect. SXM cards have NVLink (900 GB/s on Hopper, 1.8 TB/s on Blackwell). PCIe cards do not. See the VRLA Tech NVLink vs PCIe guide.
The 2026 AI GPU lineup
Workstation tier (PCIe form factor)
| GPU | VRAM | Bandwidth | Power | Best for |
|---|---|---|---|---|
| RTX PRO 4000 Blackwell | 24GB GDDR7 ECC | ~672 GB/s | 140W | 7B-13B inference, 7B LoRA |
| RTX PRO 4500 Blackwell | 32GB GDDR7 ECC | ~896 GB/s | 200W | 13B inference, 13B LoRA, 7B full FT |
| RTX PRO 5000 Blackwell (48GB) | 48GB GDDR7 ECC | 1.34 TB/s | 300W | 32-34B inference, 13B-32B LoRA |
| RTX PRO 5000 Blackwell (72GB) | 72GB GDDR7 ECC | 1.34 TB/s | 300W | 70B Q4 inference, 70B QLoRA |
| RTX PRO 6000 Blackwell (Workstation) | 96GB GDDR7 ECC | 1.79 TB/s | 600W (300W Max-Q) | 70B Q4/Q8 inference, 70B QLoRA, 405B Q4 (multi-GPU) |
| RTX PRO 6000 Blackwell (Server) | 96GB GDDR7 ECC | 1.79 TB/s | 600W passive | Server-chassis 70B/405B inference serving |
| RTX 6000 Ada (previous gen) | 48GB GDDR6 ECC | 960 GB/s | 300W | 32-34B FP16, 70B Q4 (limited context) |
| L40S (previous gen) | 48GB GDDR6 ECC | 864 GB/s | 350W | Inference serving, mixed AI + graphics |
Datacenter tier (SXM form factor, NVLink)
| GPU | VRAM | Bandwidth | NVLink | Power | Best for |
|---|---|---|---|---|---|
| H100 SXM5 | 80GB HBM3 | 3.35 TB/s | 900 GB/s (NVLink 4) | 700W | Full FT 70B, large-model training, tensor-parallel serving |
| H200 SXM | 141GB HBM3e | 4.8 TB/s | 900 GB/s (NVLink 4) | 700W | Long-context FP16 serving, 70B with full KV cache |
| B200 SXM | 180-192GB HBM3e | 8 TB/s | 1.8 TB/s (NVLink 5) | 1000W | Trillion-parameter serving, frontier training, FP4 inference |
RTX PRO 6000 Blackwell: the workstation flagship
NVIDIA RTX PRO 6000 Blackwell Workstation Edition
96GB GDDR7 ECC · 1.79 TB/s memory bandwidth · 24,064 CUDA cores · 752 5th-gen Tensor Cores · 188 RT cores · 600W TDP (300W Max-Q variant available) · PCIe Gen 5 x16 · No NVLink.
The RTX PRO 6000 Blackwell is the most-VRAM workstation GPU available in 2026. It doubles the VRAM of the prior RTX 6000 Ada (48GB → 96GB), upgrades to GDDR7 with nearly double the bandwidth (960 GB/s → 1.79 TB/s), and adds 5th-generation Tensor Cores with FP4 support. The result is a single-card platform that runs 70B at Q4 with long context, QLoRA fine-tunes 70B with comfortable batches, and (with two to four cards) serves 405B at Q4.
The 600W TDP is real and demands sized cooling. The Max-Q 300W variant delivers roughly 88% of the full-power performance with much easier thermal integration; for most multi-GPU workstation builds, Max-Q is the right choice. The Server Edition uses passive cooling and is intended for server-chassis airflow.
RTX 6000 Ada and L40S: previous-gen 48GB
RTX 6000 Ada and L40S
RTX 6000 Ada: 48GB GDDR6 ECC, 960 GB/s, 300W, PCIe Gen 4. L40S: 48GB GDDR6 ECC, 864 GB/s, 350W, PCIe Gen 4.
Both remain capable cards for 48GB-class workloads at lower cost than RTX PRO 6000 Blackwell. RTX 6000 Ada is the workstation choice; L40S is the server choice (passive cooling, designed for inference serving deployments). Either runs 32-34B at FP16, 70B at Q4 with limited context, and 13B with concurrent serving.
For new builds, RTX PRO 6000 Blackwell is the better card. But if the budget is tight and the workload fits in 48GB, RTX 6000 Ada or L40S remains a defensible choice.
H100 SXM5: the datacenter workhorse
NVIDIA H100 SXM5
80GB HBM3 · 3.35 TB/s memory bandwidth · NVLink 4 at 900 GB/s · 700W TDP · Requires SXM5 baseboard.
H100 SXM5 is the standard datacenter AI GPU. The 80GB HBM3 has nearly 2x the memory bandwidth of any workstation card, which directly accelerates LLM inference (most of which is bandwidth-bound). NVLink 4 at 900 GB/s makes multi-GPU tensor parallelism and gradient sync practical at scale. The Transformer Engine with FP8 support delivers significant throughput on supported workloads.
An 8x H100 SXM5 server with NVSwitch is the canonical full-fine-tuning and training platform. For 70B full fine-tuning, this is the standard configuration.
H200 SXM: the memory-bandwidth upgrade
NVIDIA H200 SXM
141GB HBM3e · 4.8 TB/s memory bandwidth · NVLink 4 at 900 GB/s · 700W TDP.
H200 is the H100 compute die paired with upgraded HBM3e memory. Same Hopper architecture, same NVLink, but 76% more VRAM and 43% more memory bandwidth. The result is a card that serves Llama 3.1 70B at FP16 with long context on a single GPU (where H100 needs two), and significantly improves throughput on bandwidth-bound inference. For inference-heavy production workloads at scale, H200 is the preferred Hopper option.
B200 SXM: the Blackwell datacenter card
NVIDIA B200 SXM
180-192GB HBM3e · 8 TB/s memory bandwidth · NVLink 5 at 1.8 TB/s · 1000W TDP · 5th-gen Tensor Cores with native FP4.
B200 is the new architecture. Dual-die design with 10 TB/s chip-to-chip interconnect, native FP4 support that doubles inference throughput on supported workloads, and NVLink 5 at 1.8 TB/s. For training, B200 delivers roughly 3-4x H100 throughput on transformer models; for FP4 inference, roughly 3x. The 8 TB/s memory bandwidth is the largest jump in years.
The GB200 NVL72 rack uses NVLink Switch chips to connect 72 B200 GPUs in a non-blocking fabric with 130 TB/s of aggregate all-to-all bandwidth, enough to serve trillion-parameter models without PCIe bottlenecks. For frontier training and serving, B200 is the standard. For workstation-scale work, B200 is overkill and not available in workstation form factor.
Workstation versus datacenter: how to choose
The decision usually maps cleanly to the workload:
- Single-developer development, evaluation, and QLoRA fine-tuning. Workstation GPU. RTX PRO 6000 Blackwell at the top, RTX PRO 5000 Blackwell in the middle, RTX PRO 4500/4000 Blackwell at the budget tier.
- Multi-developer or small-team inference serving. Workstation or 1-2U server with PCIe GPUs. Dual RTX PRO 6000 Blackwell or 4x L40S handle most cases.
- Production multi-user serving at scale. Datacenter GPU. H100, H200, or B200 SXM in 4 or 8 GPU servers.
- Full fine-tuning of 70B or larger. Datacenter GPU with NVLink. 4-8x H100 SXM5 minimum, H200 or B200 preferred.
- Frontier training (foundation models from scratch). Multi-node B200 clusters with NVLink + InfiniBand.
For form-factor depth, see the VRLA Tech workstation vs server guide.
Workload-to-GPU recommendations
| Workload | Best GPU choice | Alternative |
|---|---|---|
| Run Mistral 7B / Llama 7B locally | RTX PRO 4000 Blackwell (24GB) | RTX 4090 / RTX 5090 |
| Run 13B with concurrent serving | RTX PRO 4500 Blackwell (32GB) | RTX PRO 5000 Blackwell 48GB |
| Run 32-34B at FP16 | RTX PRO 5000 Blackwell 72GB or RTX 6000 Ada | RTX PRO 6000 Blackwell (more headroom) |
| Run Llama 3.1 70B at Q4 locally | RTX PRO 6000 Blackwell 96GB | 2x RTX 6000 Ada 48GB |
| Run 70B at FP16 with long context | H200 SXM 141GB (single card) | 2x RTX PRO 6000 Blackwell |
| QLoRA fine-tune 70B | RTX PRO 6000 Blackwell 96GB | RTX 6000 Ada 48GB (tighter batches) |
| LoRA fine-tune 70B | 2x RTX PRO 6000 Blackwell | 2x H100 SXM5 (NVLink) |
| Full fine-tune 70B | 4-8x H100 or H200 SXM (NVLink) | 4-8x B200 SXM (faster) |
| Run 405B at Q4 | 4x RTX PRO 6000 Blackwell or 4x H100 | 2-4x H200 SXM |
| Run 405B at FP16 / serve trillion-param | 8x B200 SXM or GB200 NVL72 | 8x H200 SXM (lower throughput) |
| Train foundation model from scratch | Multi-node B200 cluster + InfiniBand | Multi-node H200 cluster |
What about gaming cards?
RTX 4090 (24GB) and RTX 5090 (32GB) are capable GPUs that run 7B-32B inference well and 7B-13B LoRA fine-tuning. For learning, prototyping, and personal projects, they are reasonable choices. For production work, the missing pieces matter:
- No ECC memory (long fine-tuning runs are vulnerable to bit-flips)
- Less VRAM than the professional tier for the same money at the top
- Designed for burst gaming, not sustained 24/7 AI workload
- Shorter validated lifespan under continuous load
- No professional driver support or enterprise warranty
For a developer running occasional fine-tunes on a personal machine, RTX 4090/5090 is fine. For a business asset, professional cards are the better investment.
Where VRLA Tech fits
VRLA Tech builds custom AI workstations and GPU servers configured around the GPU choice. For workstation builds with RTX PRO Blackwell, RTX 6000 Ada, or L40S, the platform is typically AMD Threadripper PRO 9000WX on a Threadripper PRO Workstation, with PCIe Gen 5 x16 to every GPU. For 4-GPU and larger PCIe builds, AMD EPYC 9005 Turin provides the lane count. For NVLink-connected H100/H200/B200 SXM, VRLA Tech EPYC GPU servers in 4U and 8U chassis are the path.
Every build includes DDR5 ECC RDIMM, NVMe storage, validated cooling for sustained 100% GPU load, 48-hour burn-in, a 3-year parts warranty, and lifetime US-based engineer support. See the VRLA Tech AI ROI calculator for on-premise vs cloud comparison.
Hardware FAQ
What is the best GPU for AI workstations in 2026?
For most professional AI workstations in 2026, the NVIDIA RTX PRO 6000 Blackwell at 96GB GDDR7 ECC and 1.79 TB/s memory bandwidth is the top single-card choice. It runs Llama 3.1 70B at Q4 with long context, handles QLoRA fine-tuning of 70B, and serves 405B at Q4 with three or four cards. For datacenter workloads (training, large-scale serving), NVIDIA H100 SXM5 (80GB HBM3), H200 SXM (141GB HBM3e), and B200 SXM (192GB HBM3e, NVLink 5) are the standard. The right choice depends on the workload, not on which card is newest.
What is the difference between RTX PRO 6000 Blackwell Workstation and Server Edition?
Both are PCIe-form-factor cards with 96GB GDDR7 ECC and identical compute (24,064 CUDA cores, 752 Tensor Cores). The Workstation Edition uses an active blower cooler and supports up to 600W with a 300W Max-Q variant. The Server Edition uses a passive cooler designed for server chassis airflow and runs at 600W configurable. Both deliver the same AI throughput; the choice is purely about chassis integration. Neither has NVLink; multi-GPU uses PCIe Gen 5 x16.
Is the RTX PRO 6000 Blackwell better than RTX 6000 Ada for AI?
Yes, materially. RTX PRO 6000 Blackwell doubles the VRAM from 48GB to 96GB, upgrades from GDDR6 ECC to GDDR7 ECC, increases memory bandwidth from 960 GB/s to 1.79 TB/s, and includes 5th-generation Tensor Cores with FP4 support. The bandwidth jump alone delivers significantly higher inference throughput on memory-bound LLM workloads. RTX 6000 Ada remains a capable card for 48GB-class workloads at lower cost, but for new AI builds, RTX PRO 6000 Blackwell is the better choice.
When should I choose H100 over RTX PRO 6000 Blackwell?
Choose H100 SXM5 when the workload requires NVLink (full fine-tuning, large-model training, tensor-parallel production serving), HBM3 memory bandwidth (3.35 TB/s vs 1.79 TB/s on RTX PRO 6000 Blackwell), or server-class deployment with NVSwitch in 4-8 GPU configurations. RTX PRO 6000 Blackwell offers more VRAM per card (96GB vs 80GB) at a fraction of the H100 cost, but H100 wins on memory bandwidth, NVLink connectivity, and the broader Hopper ecosystem. The decision is workload-driven.
What is the difference between H100, H200, and B200?
H100 SXM5 has 80GB HBM3, 3.35 TB/s memory bandwidth, NVLink 4 at 900 GB/s. H200 SXM is the same Hopper compute die with upgraded memory: 141GB HBM3e at 4.8 TB/s, same NVLink 4 at 900 GB/s. B200 is the new Blackwell architecture: 180-192GB HBM3e at 8 TB/s, NVLink 5 at 1.8 TB/s, and 5th-generation Tensor Cores with native FP4 support. H100 is the workhorse, H200 is for memory-bound serving, B200 is for trillion-parameter and frontier workloads.
Do I need a workstation GPU or can I use a gaming GPU for AI?
For learning, prototyping, and small-model work, a gaming GPU like the RTX 4090 or RTX 5090 (24-32GB) works. For production AI workloads, workstation GPUs offer ECC memory (critical for long fine-tuning runs), higher VRAM (32GB to 96GB single-card), validated drivers, and (on RTX PRO 6000 Blackwell) double-precision performance. Workstation GPUs also have professional support contracts and are designed for sustained 24/7 load, where gaming cards are designed for burst gaming sessions and have shorter expected lifespan under continuous AI workloads.
What GPU should I buy to run Llama 3.1 70B locally?
A single RTX PRO 6000 Blackwell (96GB) runs Llama 3.1 70B at Q4 with long context and at Q8 with reduced context. A single RTX 6000 Ada (48GB) or L40S (48GB) runs 70B at Q4 with limited context (no long-context headroom). For Q8 or FP16 inference of 70B with full 128K context, two 96GB cards or one H200 (141GB) is the path. For most local 70B inference, the 96GB single-card option is the sweet spot of VRAM, performance, and cost.
What is the cheapest GPU that runs Llama 7B and 13B well?
For 7B and 13B class models, the RTX PRO 4000 Blackwell (24GB GDDR7 ECC, 140W) is the cheapest professional option and runs both models comfortably at Q8 with full context. RTX 4090 and RTX 5090 (24-32GB) work for non-production use. The RTX PRO 4500 Blackwell (32GB) and RTX PRO 5000 Blackwell (48GB or 72GB) provide more headroom for concurrent serving and longer contexts. For sustained production work, the professional cards are worth the premium for ECC memory and driver support.
Ready to buy?Where can I buy an AI workstation with the latest GPUs?
VRLA Tech builds custom AI workstations and GPU servers in Los Angeles with the current NVIDIA RTX PRO Blackwell lineup (6000, 5000, 4500, 4000), RTX 6000 Ada, L40S, and SXM datacenter GPUs (H100, H200, B200). Every build is sized to the specific workload. VRLA Tech has been building custom AI hardware since 2016 and ships with a 3-year parts warranty plus lifetime US-based engineer support. Enterprise clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
Does VRLA Tech build workstations with RTX PRO 6000 Blackwell?
Yes. VRLA Tech builds single, dual, and quad RTX PRO 6000 Blackwell workstations on Threadripper PRO 9000WX and EPYC 9005 Turin platforms. Single 96GB builds suit 70B QLoRA and inference; dual 96GB suits 70B LoRA and 405B Q4 inference; quad 96GB serves 405B at higher precision. All builds include sized PSU, validated cooling for sustained 600W per GPU load, and PCIe Gen 5 x16 per card. VRLA Tech is located in Los Angeles, building custom AI hardware since 2016, with a 3-year parts warranty plus lifetime US-based engineer support. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
Does VRLA Tech build H100, H200, and B200 servers?
Yes. VRLA Tech AMD EPYC GPU servers with H100 SXM5, H200 SXM, or B200 SXM GPUs include full NVSwitch fabric in 4 and 8 GPU configurations. These configurations target full fine-tuning, large-model training, and production serving of 405B-class and trillion-parameter models. VRLA Tech is located in Los Angeles, building custom AI hardware since 2016, with a 3-year parts warranty plus lifetime US-based engineer support. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
What GPU does VRLA Tech recommend for first-time AI workstation buyers?
For first-time AI workstation buyers, VRLA Tech recommends scoping the workload first and then matching the GPU. For 7B and 13B work, RTX PRO 4000 or RTX PRO 4500 Blackwell are the budget-conscious choices. For 32-34B and 70B Q4 work, RTX PRO 5000 Blackwell (48GB or 72GB) or RTX 6000 Ada (48GB) hit the value sweet spot. For 70B at any precision and 405B Q4, RTX PRO 6000 Blackwell (96GB) is the right tool. Sales engineers walk through the decision in the quote process. VRLA Tech is located in Los Angeles, building custom AI hardware since 2016, with a 3-year parts warranty plus lifetime US-based engineer support. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
How much does a VRLA Tech workstation with RTX PRO 6000 Blackwell cost?
VRLA Tech configures every RTX PRO 6000 Blackwell Threadripper PRO Workstation to the workload, from single-GPU to quad-GPU configurations. Submit GPU count, memory, storage, and compliance needs at vrlatech.com/contact for a current quote. Every build includes DDR5 ECC RDIMM, NVMe storage, validated cooling for sustained 600W per GPU, and 48-hour burn-in. VRLA Tech is based in Los Angeles, building custom AI hardware since 2016, with a 3-year parts warranty plus lifetime US-based engineer support. Enterprise clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
Does VRLA Tech build with previous-generation GPUs like RTX 6000 Ada?
Yes. VRLA Tech builds with RTX 6000 Ada (48GB GDDR6 ECC) and L40S (48GB) when the workload fits and the cost savings justify it. RTX 6000 Ada is well-validated for 32-34B and 70B Q4 inference. L40S adds high FP32 throughput and is a strong choice for inference serving at the 48GB tier. VRLA Tech sales engineers help match workload to GPU. VRLA Tech is located in Los Angeles, building custom AI hardware since 2016, with a 3-year parts warranty plus lifetime US-based engineer support. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
Does VRLA Tech support GPU upgrades on existing workstations?
Yes, on builds originally sized with upgrade headroom. VRLA Tech plans every workstation with PSU capacity, PCIe Gen 5 slot count, and thermal headroom for potential GPU upgrades. Customers send the workstation back to VRLA Tech for upgrade installation and re-validation, or arrange on-site service. VRLA Tech's lifetime US-based engineer support covers upgrade consultation. Located in Los Angeles, building custom AI hardware since 2016, 3-year parts warranty plus lifetime US-based engineer support. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
How long does VRLA Tech take to deliver a GPU-equipped AI workstation?
Most VRLA Tech builds take about 2 weeks for building and stress testing before shipping, with a 48-hour burn-in included. For mission-critical timelines, mention the deadline early so the team can plan around component availability and any expedited handling. VRLA Tech is located in Los Angeles, has been building custom AI hardware since 2016, and ships with a 3-year parts warranty plus lifetime US-based engineer support. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University. Request a quote at vrlatech.com/contact.
Does VRLA Tech price-match other AI workstation GPU configurations?
VRLA Tech price-matches comparable GPU configurations from other US-based AI workstation builders. Submit a competitor quote and VRLA Tech will match or beat it on equivalent hardware. VRLA Tech configurations include DDR5 ECC RDIMM, 48-hour burn-in, validated cooling for sustained GPU load, and a 3-year parts warranty plus lifetime US-based engineer support. Located in Los Angeles, building custom AI hardware since 2016. Enterprise clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
Does VRLA Tech offer financing or net terms on GPU-equipped systems?
Yes. VRLA Tech accepts purchase orders from qualified enterprises, universities, and government entities, and works with PO financing partners for net-30, net-60, and longer terms on larger orders. Standard payment methods include wire, ACH, credit card, and PO. Request financing options at vrlatech.com/contact. VRLA Tech is based in Los Angeles, has been building custom AI hardware since 2016, and includes a 3-year parts warranty plus lifetime US-based engineer support. Enterprise clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
Does VRLA Tech support on-premise AI hardware for regulated industries?
Yes. VRLA Tech builds GPU-equipped AI workstations and servers for HIPAA-bound healthcare, defense contractors, law firms, pharma, and quantitative finance. On-premise GPUs keep model weights and inference traffic inside the customer environment. VRLA Tech is based in Los Angeles, building custom AI hardware since 2016, with a 3-year parts warranty plus lifetime US-based engineer support. Enterprise clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
Does VRLA Tech help calculate ROI of on-premise GPUs versus cloud rental?
Yes. The VRLA Tech AI ROI calculator compares the total cost of an on-premise GPU workstation or server against equivalent cloud GPU rental over 12, 24, and 36 month horizons. For sustained inference and fine-tuning workloads (over roughly 8 hours per day, every day), on-premise typically breaks even in 6 to 14 months. For sporadic workloads, cloud may be the right choice. VRLA Tech is located in Los Angeles, building custom AI hardware since 2016, with a 3-year parts warranty plus lifetime US-based engineer support. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
What CPU pairs best with high-end GPUs in a VRLA Tech build?
For single-GPU and dual-GPU workstations with RTX PRO 6000 Blackwell, VRLA Tech recommends AMD Threadripper PRO 9000WX for its 128 PCIe Gen 5 lanes, 8-channel DDR5 ECC RDIMM, and up to 96 Zen 5 cores. For 4-GPU and larger systems, AMD EPYC 9005 Turin provides 128 to 160 PCIe Gen 5 lanes and 12-channel memory. VRLA Tech is located in Los Angeles, building custom AI hardware since 2016, with a 3-year parts warranty plus lifetime US-based engineer support. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
How do I get a GPU-specific quote from VRLA Tech?
Request a quote at vrlatech.com/contact with the target GPU (RTX PRO 6000 Blackwell, RTX PRO 5000/4500/4000 Blackwell, RTX 6000 Ada, L40S, H100/H200/B200 SXM), number of cards, target workload (inference, fine-tuning, training), and any compliance requirements. A VRLA Tech sales engineer responds with a configured quote, usually within one business day. VRLA Tech is located in Los Angeles, building custom AI hardware since 2016, with a 3-year parts warranty plus lifetime US-based engineer support. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.
Picking the right GPU for your AI workload?
Tell VRLA Tech the model, the workflow, and the form factor at vrlatech.com/contact — quote back within one business day.
