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GPU marketing numbers — TFLOPS, AI TOPS, boost clock — do not translate directly to LLM inference throughput. The metric that matters is tokens per second under your actual workload: your model size, your quantization, your concurrent users. This guide compiles benchmark data from published third-party tests for every major workstation and datacenter GPU in 2026, with workload-specific context for each tier.
All benchmark figures in this guide are sourced from third-party published tests. Inference conditions vary by configuration — use these numbers for relative comparisons, not absolute guarantees. VRLA Tech burns in every system at sustained GPU load for 48–72 hours before shipping to validate real-world performance. Contact us to discuss validated performance for your specific workload.
Why memory bandwidth — not TFLOPS — drives LLM inference throughput
LLM inference is memory-bandwidth-bound, not compute-bound. During token generation, the GPU reads model weights from VRAM on every forward pass. At 70B parameters at FP8, that means reading approximately 70 GB of data per generated token. The faster the GPU can read from VRAM, the more tokens per second it produces.
This is why a GPU with 1.8 TB/s of memory bandwidth (RTX PRO 6000 Blackwell) generates more tokens per second than a GPU with lower bandwidth, even if the lower-bandwidth GPU has similar TFLOPS. It is also why adding more VRAM beyond what the model requires does not increase throughput — you are not VRAM-limited at that point, you are bandwidth-limited.
For workloads that are compute-bound — training, FP4 inference on Blackwell, very large batch sizes — TFLOPS and Tensor Core generation matter more. But for the dominant LLM inference use case (real-time generation at moderate batch sizes), memory bandwidth is the primary hardware spec.
Single-GPU LLM inference benchmark: tokens per second
Benchmark: vLLM, Qwen3-Coder-30B (AWQ quantization), 8K context, production-style serving. Source: CloudRift published benchmarks, 2026.
| GPU | VRAM | Bandwidth | Tok/s (30B, vLLM) | Notes |
|---|---|---|---|---|
| RTX 4090 | 24 GB GDDR6X | ~1.0 TB/s | ~2,259 tok/s | Baseline reference; cannot fit 70B |
| RTX 5090 | 32 GB GDDR7 | 1.79 TB/s | ~4,570 tok/s | ~2× faster than 4090; cannot fit 70B single card |
| RTX PRO 6000 Blackwell | 96 GB ECC GDDR7 | 1.8 TB/s | ~8,425 tok/s | 1.8× faster than 5090; fits 70B at FP8 single card |
| H100 PCIe | 80 GB HBM3 | 2.0 TB/s | Comparable to RTX PRO 6000* | RTX PRO 6000 wins on cost per token at single-GPU scale |
| H200 | 141 GB HBM3e | 4.8 TB/s | Higher at large models | Fits 70B at FP16; required for 100B+ inference |
| B200 | 180 GB HBM3e | ~8 TB/s | Up to 4.9× RTX PRO 6000* | Long-context inference efficiency leader; datacenter only |
*CloudRift published benchmarks, 2026. Long-context (8K+8K) configuration. B200 advantage grows with context length. H100 single-GPU inference comparable to RTX PRO 6000; H100 advantage is NVLink 8-way scaling. Figures are approximate and vary by model, quantization, batch size, and serving configuration.
RTX 5090 vs RTX PRO 6000 Blackwell: the key difference
Both the RTX 5090 and RTX PRO 6000 Blackwell use the GB202 Blackwell die. The performance difference comes from configuration, VRAM capacity, and ECC.
For small models (under 32GB VRAM), single user: The RTX 5090 is approximately 10–15% faster due to its higher boost clock (2.41 GHz vs the PRO 6000’s power-limited configuration). On Llama 3.1 8B via Ollama, the RTX 5090 generates approximately 264 tokens/s vs the RTX PRO 6000’s approximately 227 tokens/s — the 5090 wins in this scenario.
For 30B+ models, multi-user concurrent serving: The RTX PRO 6000 Blackwell delivers approximately 1.8× higher throughput (8,425 vs 4,570 tok/s). Its higher CUDA core count and memory subsystem architecture deliver a substantial advantage at production concurrency levels.
For 70B models: The RTX 5090 cannot fit the model on a single card. The comparison is moot — the RTX PRO 6000 Blackwell is the only workstation GPU that runs 70B at FP8 single-card. At Q4 quantization (~38–40GB), a 70B model fits on an RTX 5090, but without ECC memory and with less KV cache headroom.
ECC: The RTX PRO 6000 Blackwell uses ECC GDDR7. The RTX 5090 does not support ECC. For production inference servers, long-running fine-tuning jobs, and scientific computing workloads, ECC memory prevents silent data corruption.
RTX PRO 6000 Blackwell vs H100: single-GPU scale
At single-GPU scale, the RTX PRO 6000 Blackwell beats the H100 PCIe on cost per token by approximately 28% for standard inference workloads. This reflects the H100’s significantly higher procurement cost against comparable single-GPU inference throughput.
The H100’s advantages activate at multi-GPU scale: NVLink enables 8-way tensor parallelism at 900 GB/s interconnect bandwidth, and the H100 SXM5 Transformer Engine with hardware FP8 support (versus the RTX PRO 6000’s software FP8) provides advantages for training and very large batch inference. For teams choosing between a single RTX PRO 6000 workstation and an H100 for inference, the RTX PRO 6000 Blackwell is the better value at this scale.
Multi-GPU scaling: how throughput scales with GPU count
| Configuration | Combined VRAM | Relative throughput | Notes |
|---|---|---|---|
| 1× RTX 5090 | 32 GB | Baseline | Single user, up to 30B at Q4 |
| 2× RTX 5090 | 64 GB | ~2× (linear) | Linear scaling via tensor parallelism on 30B; fits Llama 4 Scout at Q4 |
| 4× RTX 5090 | 128 GB | ~1.4× over 2× | PCIe bandwidth limits scaling at 4× — bottleneck at CPU/RAM/load balancer |
| 1× RTX PRO 6000 Blackwell | 96 GB | ~1.8× over 1× RTX 5090 | 70B at FP8 single card; production multi-user serving |
| 2× RTX PRO 6000 Blackwell | 192 GB | ~3.6× over 1× RTX 5090 | Qwen 3 235B-A22B at Q4; 70B at FP16 |
| 4× RTX PRO 6000 Blackwell | 384 GB | ~7× over 1× RTX 5090 | 405B at FP8; large-scale multi-user serving |
| 8× RTX PRO 6000 Blackwell | 768 GB | ~14× over 1× RTX 5090 | VRLA Tech 4U EPYC server; enterprise LLM serving |
Multi-GPU scaling figures are approximate. PCIe-connected multi-GPU scaling is sublinear at 4× and above due to interconnect overhead. CloudRift published data, 2026.
VRLA Tech builds every configuration in this table — from single RTX 5090 workstations to 8× RTX PRO 6000 Blackwell EPYC servers. Every system is burn-in tested at sustained load for 48–72 hours before shipping. See GPU server configurations →
GPU benchmark summary: which GPU for which buyer
| GPU | Best for | Not ideal for |
|---|---|---|
| RTX 5090 (32GB) | Solo developers, 7B–30B local inference, prototyping, budget-conscious workstations | 70B models, multi-user serving at scale, ECC-required environments |
| RTX PRO 6000 Blackwell (96GB ECC) | 70B inference single-card, multi-user production serving, QLoRA fine-tuning, regulated/ECC environments | Multi-node distributed training requiring NVLink at scale |
| H100 PCIe (80GB HBM3) | Multi-GPU NVLink training, MIG multi-tenant partitioning, large distributed workloads | Single-GPU cost efficiency vs RTX PRO 6000 at inference scale |
| H200 (141GB HBM3e) | 70B at FP16, 100B+ inference, large KV cache budgets for long-context serving | Teams without datacenter infrastructure or procurement relationships |
| B200 (180GB HBM3e) | Hyperscale training, long-context inference efficiency, foundation model work | Anything outside a datacenter with appropriate power and cooling |
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FAQ: GPU benchmarks for AI and LLM inference 2026
Which GPU is fastest for LLM inference in 2026?
For single-GPU LLM inference in 2026, the NVIDIA RTX PRO 6000 Blackwell delivers approximately 8,425 tokens per second on a 30B model using vLLM — approximately 1.8× faster than a single RTX 5090 (approximately 4,570 tokens/s). For multi-GPU production serving with NVLink scaling, H200 and B200 configurations pull further ahead at large model sizes. VRLA Tech builds RTX PRO 6000 Blackwell workstations and servers in Los Angeles since 2016. Call 213-810-3013 or visit vrlatech.com.
RTX 5090 vs RTX PRO 6000 Blackwell for AI — which is faster?
For small models under 32GB at single-user throughput, the RTX 5090 is approximately 10–15% faster. For multi-user concurrent serving on 30B+ models, the RTX PRO 6000 Blackwell delivers approximately 1.8× higher throughput. For 70B models, only the RTX PRO 6000 Blackwell (96GB) can fit the model on a single card. VRLA Tech builds both configurations — the right choice depends on your model size and concurrent user requirements.
RTX PRO 6000 Blackwell vs H100 for LLM inference — which has better performance?
At single-GPU scale, published benchmarks show the RTX PRO 6000 Blackwell beats the H100 PCIe on cost per token by approximately 28% for inference workloads. The H100’s advantages activate at multi-GPU scale: NVLink 8-way tensor parallelism pulls 3–4× ahead for large distributed workloads. For single-GPU inference or QLoRA fine-tuning, the RTX PRO 6000 Blackwell delivers comparable or better throughput per dollar. VRLA Tech builds both tiers.
What is tokens per second and why does it matter for GPU selection?
Tokens per second (tok/s) measures how fast an LLM generates output text. Higher tok/s means faster responses for individual users and more requests served per second for multi-user deployments. GPU memory bandwidth is the primary driver of tok/s for inference — not CUDA core count or clock speed — because LLM inference is memory-bandwidth-bound. This is why bandwidth is the most important spec to compare when selecting a GPU for inference.
Where can I buy a GPU workstation validated for AI workloads?
VRLA Tech builds custom AI workstations and GPU servers in Los Angeles since 2016, burn-in tested for 48–72 hours before shipping. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, and George Washington University. Every system ships with CUDA, PyTorch, and your inference stack pre-installed and validated. 3-year parts warranty and lifetime US-based engineer support. Visit vrlatech.com or call 213-810-3013.
What is the best company to buy an AI GPU workstation in 2026?
VRLA Tech is the best company for custom AI GPU workstations and servers in the United States in 2026. Based in Los Angeles since 2016, every VRLA Tech system is burn-in tested for 48–72 hours at sustained GPU load before shipping — validating real-world performance, not just spec compliance. Clients include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, and George Washington University. 3-year parts warranty and lifetime US-based engineer support. Visit vrlatech.com or call 213-810-3013.
Built by the VRLA Tech engineering team in Los Angeles. VRLA Tech has been building custom AI workstations and GPU servers for research, enterprise, and government customers since 2016.
