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AI system category

Professional AI Workloads

High-end workstation options for serious AI workloads and large projects; unsupported multi-GPU systems remain custom quote paths.

Pricingconfirmed by quote

Best for

  • 70B-class compatibility checks with quantization and context caveats
  • Single-GPU catalog systems and custom multi-GPU quotes
  • ECC RAM, high VRAM, and workstation cooling

Not ideal for

  • Much higher cost and power draw
  • Physically larger and louder under load
  • Unnecessary for basic local chat or small models

AI fit is a rough estimate; model/runtime/quantization/context affects results.

Workstation / custom quote path

LLMLab Forge

Configuration: RTX PRO 4500 32GB AI Workstation

A quiet, supportable workstation for professional local AI, development, and creator workloads.

GPU: PNY NVIDIA RTX PRO 4500 Blackwell 32GB Small Box

CPU: AMD Ryzen 7 9700X

RAM: 64 GB | Storage: 4000 GB

Recommended model class: 32 GB ECC GPU memory for larger inference jobs and professional CUDA software.

30B-class Q4 only with caveats

Fit assumes quantization, moderate context, and runtime validation; 70B is not a normal target without 48GB+ VRAM or large unified memory.

€9,683

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Workstation / custom quote path

LLMLab Forge Pro

Configuration: RTX PRO 5000 72GB AI Workstation

A high-memory professional workstation for large local AI models, demanding creative work, and serious technical projects.

GPU: PNY NVIDIA RTX PRO 5000 72GB Blackwell Small Box

CPU: AMD Ryzen 9 9950X

RAM: 64 GB | Storage: 2000 GB

Recommended model class: 70B-class quantized local models, rendering, and heavy creator or engineering workloads.

70B-class target requires validation

Selected 70B-class Q4 models need high VRAM or Apple unified memory, short-to-moderate context, and runtime validation.

€15,574

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Workstation / custom quote path

LLMLab Forge Ultra

Configuration: RTX PRO 6000 96GB AI Workstation Max

A maximum-capacity single-GPU workstation for large models, datasets, rendering, and demanding CUDA projects.

GPU: PNY NVIDIA RTX PRO 6000 Blackwell Workstation Edition 96GB

CPU: AMD Ryzen Threadripper PRO 9975WX

RAM: 256 GB | Storage: 8000 GB

Recommended model class: 96 GB ECC VRAM, 256 GB eight-channel ECC RAM, and 8 TB fast storage.

70B-class target requires validation

Selected 70B-class Q4 models need high VRAM or Apple unified memory, short-to-moderate context, and runtime validation.

€34,667

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Workstation / custom quote path

48GB ECC CUDA Workstation

This is the lower-cost professional CUDA alternative to the recommended 72GB workstation. It retains a 48GB ECC RTX PRO 5000, 64GB of system memory, and 4TB of TLC storage for 30B/34B workloads and selected 70B-class experiments after validation.

GPU: PNY NVIDIA RTX PRO 5000 Blackwell 48GB

CPU: AMD Ryzen 9 9950X

RAM: 64 GB | Storage: 4000 GB

Recommended model class: 30B/34B high headroom; selected 70B q4 after workload-fit validation

70B-class target requires validation

Selected 70B-class Q4 models need high VRAM or Apple unified memory, short-to-moderate context, and runtime validation.

€13,962

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Local AI examples

Good fit for private chatGood fit for coding helpGood fit for document summaries70B-class models need strict caveats

Recommended model

Qwen3.6 35B-A3B

A capable MoE model that gives 32GB and 48GB workstations a meaningfully heavier coding target.

Comfortable GPU fit

Usable

ollama run qwen3.6:35b

Likely good memory headroom for this quantized model at normal context sizes.

  • 256GB system/unified memory available
  • 96GB effective accelerator memory for model weights and cache

Qwen3.6 27B

Serious coding help, complex reasoning, and longer document analysis

Comfortable GPU fit

A strong dense model for 24GB to 32GB GPUs and higher-memory Apple systems.

Expected experience: Fast

Likely good memory headroom for this quantized model at normal context sizes.

  • 256GB system/unified memory available

Qwen3.5 9B

Private chat, coding help, document summaries, and image questions

Comfortable GPU fit

A strong everyday model for 12GB-class GPUs and a practical coding pick when speed matters.

Expected experience: Fast

Likely good memory headroom for this quantized model at normal context sizes.

  • 256GB system/unified memory available
Expandable technical details

Assumptions

  • GPU VRAM assumption: 96GB from PNY NVIDIA RTX PRO 6000 Blackwell Workstation Edition 96GB.
  • System RAM: 256GB.
  • Ratings include model weights, estimated KV cache, runtime overhead, and safety margin for one local model running at a time. Treat them as fit guidance, not a speed guarantee.
  • Profile page uses a representative listed build. Open a build detail page for exact component-level fit.
Qwen3.6 35B-A3B technical details

Family: Qwen3.6

Parameters: 35B

Structure: 35B total / 3B active MoE

License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.

Native context: 262,144 tokens

Extended context: 1,010,000 via YaRN; not a default fit assumption

Quantization: Q4_K_M

Approx. Q4 weights: 24 GB

Default estimate: 31.5 GB @ 8,192 tokens

Weights / KV / runtime / margin: 24 GB / 2.5 GB / 2 GB / 3 GB

CPU/RAM fallback: Not recommended

VRAM: 32 GB minimum / 48 GB recommended

RAM: 64 GB minimum / 64 GB recommended

Current run mode: Comfortable GPU fit

Expected experience: Usable

Full GPU offload: Only when the memory estimate and context fit

Context warning: Long repository context can use the remaining headroom quickly, especially on 32GB GPUs.

ContextWeightsKVRuntimeMarginEstimated GPU memory
4K24 GB1.5 GB2 GB3 GB30.5 GB
8K24 GB2.5 GB2 GB3 GB31.5 GB
16K24 GB5 GB2 GB3.5 GB34.5 GB
32K24 GB10 GB2 GB4 GB40 GB

Swipe the table sideways to inspect every memory estimate.

Research sources

Qwen3.6 35B-A3B official model cardOllama Qwen3.6 tags

Researched: 2026-07-30

Qwen3.6 27B technical details

Family: Qwen3.6

Parameters: 27B

Structure: 27B dense

License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.

Native context: 262,144 tokens

Extended context: 1,010,000 via YaRN; not a default fit assumption

Quantization: Q4_K_M

Approx. Q4 weights: 17 GB

Default estimate: 23 GB @ 8,192 tokens

Weights / KV / runtime / margin: 17 GB / 2 GB / 1.5 GB / 2.5 GB

CPU/RAM fallback: Not recommended

VRAM: 24 GB minimum / 32 GB recommended

RAM: 48 GB minimum / 64 GB recommended

Current run mode: Comfortable GPU fit

Expected experience: Fast

Full GPU offload: Only when the memory estimate and context fit

Context warning: Repository-scale or very long document context can push a 24GB card beyond a comfortable fit.

ContextWeightsKVRuntimeMarginEstimated GPU memory
4K17 GB1 GB1.5 GB2.5 GB22 GB
8K17 GB2 GB1.5 GB2.5 GB23 GB
16K17 GB4 GB1.5 GB3 GB25.5 GB
32K17 GB8 GB1.5 GB3.5 GB30 GB

Swipe the table sideways to inspect every memory estimate.

Research sources

Qwen3.6 27B official model cardOllama Qwen3.6 tags

Researched: 2026-07-30

Qwen3.5 9B technical details

Family: Qwen3.5

Parameters: 9B

Structure: 9B dense

License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.

Native context: 262,144 tokens

Extended context: 1,010,000 via YaRN; not a default fit assumption

Quantization: Q4_K_M

Approx. Q4 weights: 6.6 GB

Default estimate: 11 GB @ 8,192 tokens

Weights / KV / runtime / margin: 6.6 GB / 1.5 GB / 1.2 GB / 1.5 GB

CPU/RAM fallback: Not recommended

VRAM: 8 GB minimum / 12 GB recommended

RAM: 16 GB minimum / 32 GB recommended

Current run mode: Comfortable GPU fit

Expected experience: Fast

Full GPU offload: Only when the memory estimate and context fit

Context warning: Start around 8K context even though the model supports much more.

ContextWeightsKVRuntimeMarginEstimated GPU memory
4K6.6 GB1 GB1.2 GB1.5 GB10.5 GB
8K6.6 GB1.5 GB1.2 GB1.5 GB11 GB
16K6.6 GB2.5 GB1.2 GB1.5 GB12 GB
32K6.6 GB5 GB1.2 GB2 GB15 GB

Swipe the table sideways to inspect every memory estimate.

Research sources

Qwen3.5 9B official model cardOllama Qwen3.5 tags

Researched: 2026-07-30

Local AI performance is approximate. Results depend on quantization, context length, backend, drivers, and whether the model plus KV cache fits in VRAM or Apple unified memory.