By VRLA Tech  ·  Case Study  ·  June 2026

A full-stack AI developer needed a single workstation capable of running local LLM inference on models up to 70B, LoRA and QLoRA fine-tuning on 7B to 30B models, Flux.1 and SDXL image generation through ComfyUI, audio model fine-tuning with audiocraft and stable-audio-tools, and light reinforcement learning experimentation — often with multiple models loaded simultaneously. This is the system VRLA Tech built.

The workload: a full-stack AI development environment with no single primary task

Most AI workstation builds optimize for one primary workload. This one did not have one. The developer’s stack was genuinely broad:

• Local LLM inference: Llama 4, Qwen 3, and open-source models up to 70B parameters (quantized), running through LangGraph agentic pipelines, Pydantic AI, Anthropic and OpenAI SDKs, and MCP tool integrations.
• LoRA and QLoRA fine-tuning: 7B to 13B models regularly, occasionally up to 30B, using Hugging Face PEFT and PyTorch.
• Image generation: Flux.1 and SDXL via ComfyUI for generative image workflows.
• Audio model fine-tuning and generation: RAVE, stable-audio-tools, MusicGen via Meta’s audiocraft, and PyTorch Audio pipelines.
• Reinforcement learning experimentation: Light RL work with game-playing agents.
• Concurrent multi-model operation: The developer frequently runs an LLM alongside an image generation model and an audio model simultaneously — a workload pattern that puts pressure on both VRAM and system RAM.

The challenge is that each of these workloads, taken individually, is well-served by a 24GB consumer GPU. Taken together — especially the concurrent multi-model pattern — they push against that ceiling in ways that interrupt workflow. The design goal was a machine that could hold multiple models in VRAM simultaneously without the developer managing load/unload cycles mid-session.

An honest note on 70B inference at 32GB VRAM

The customer’s stated requirement included local inference on models up to 70B (quantized). It is worth being precise about what 32GB VRAM delivers here, because this is a commonly misunderstood spec point.

A 70B dense model at Q4_K_M quantization requires approximately 39 to 45GB of VRAM including KV cache overhead — more than a single RTX 5090’s 32GB. At Q3_K_M (approximately 28 to 30GB), a 70B model fits on a single RTX 5090 with minimal headroom, but Q3 quantization introduces noticeable quality degradation on complex reasoning tasks compared to Q4.

For this customer’s use case — development, experimentation, agentic pipelines, and prototyping rather than production inference serving — the practical reality is that well-quantized models in the 30B to 34B range on an RTX 5090 deliver output quality that matches or closely approaches 70B at aggressive quantization, while running at significantly higher token generation speed. Models like Qwen 3 32B at Q4 fit fully GPU-resident on the RTX 5090 with VRAM to spare for concurrent workloads.

For customers who specifically require high-quality 70B inference as a primary workload, VRLA Tech recommends the RTX PRO 6000 Blackwell at 96GB VRAM, which handles 70B at Q4 and Q5 fully GPU-resident without compromise.

The build: what VRLA Tech configured and why

Why Ryzen 9 9950X

The 9950X brings 16 Zen 5 cores and 32 threads on the AM5 platform with dual-channel DDR5-5600 support. VRLA Tech builds the AMD Ryzen Workstation on this platform for high-frequency professional and AI development workloads. For an AI development workstation running LangGraph agents, MCP tool servers, PyTorch Audio pipelines, and ComfyUI processes concurrently alongside GPU inference, CPU thread count determines how smoothly the orchestration layer runs while the GPU is loaded. The 9950X’s 5.7GHz single-core boost also benefits agentic Python runtimes, which frequently hit single-threaded Python execution bottlenecks despite being GPU-backed. For a developer who also runs light RL training — Mahjong agents and similar — 16 high-frequency cores handle environment simulation and policy update loops without becoming a bottleneck against GPU rollout generation.

Why NVIDIA RTX 5090 at 32GB

The RTX 5090 is the highest-VRAM consumer GPU available in 2026 at 32GB GDDR7. Its 1,792 GB/s memory bandwidth — a 78% improvement over the RTX 4090 — directly translates to faster token generation on LLM inference workloads, which are memory-bandwidth-bound rather than compute-bound at most model sizes. For this developer’s concurrent workload pattern, 32GB enables combinations like: Flux.1 FP8 (approximately 13GB) plus a 7B LLM at Q4 (approximately 4-5GB) plus a MusicGen audio model loaded simultaneously — a pattern that exceeds what 24GB can hold. For LoRA and QLoRA fine-tuning, 7B to 13B models train fully GPU-resident with comfortable batch size headroom. Occasional 30B QLoRA runs (4-bit base model + LoRA adapters) fit within 32GB.

Why 128GB DDR5

128GB system RAM serves two functions in this workload. First, it provides the working memory for concurrent processes: LangGraph agent state, MCP server processes, ComfyUI process memory, PyTorch Audio data pipelines, and the Python runtime environment for multiple frameworks running simultaneously. Second, it provides the CPU offload buffer for any model layers that cannot fit entirely in VRAM — particularly relevant when running 70B models at Q3 quantization, where minimal layer offloading to system RAM is expected. At 128GB, the developer has headroom for all of these without memory pressure forcing process termination mid-session.

Why 4TB NVMe

Model weights accumulate quickly across a diverse AI development environment. A 7B model at Q4 is approximately 4GB; a 30B model is approximately 17GB; Flux.1 model files are 12-24GB depending on variant; audio model checkpoints add several GB each. A developer maintaining a local model library across LLM, image, and audio domains fills storage faster than most workloads. 4TB NVMe provides enough capacity to keep a working set of models immediately accessible without constant management, while NVMe speeds ensure fast model load times — relevant when switching between model sizes during development sessions.

Burn-in testing and delivery

The system was burn-in tested for 48 hours at VRLA Tech’s Los Angeles facility under sustained GPU and CPU load before shipping. The developer received a validated system with PyTorch, CUDA, and the base driver stack confirmed working — ready for framework installation without hardware qualification time.

What this build is optimized for

• Local LLM inference: models up to 34B fully GPU-resident at Q4; 70B at Q3 with minimal offload
• LoRA and QLoRA fine-tuning on 7B to 13B models; occasional 30B QLoRA runs
• Flux.1 and SDXL image generation via ComfyUI at full speed
• Audio model fine-tuning and generation: RAVE, stable-audio-tools, MusicGen, audiocraft
• LangGraph and Pydantic AI agentic pipeline development with local inference
• MCP tool server integration and multi-agent orchestration
• PyTorch Audio preprocessing and training pipelines
• Light RL experimentation and agent training
• Concurrent multi-model operation: LLM + image generation + audio model loaded simultaneously

Built and configured by the VRLA Tech engineering team in Los Angeles. VRLA Tech has been building custom AI workstations and GPU servers for developers, researchers, and enterprise teams since 2016.