NVIDIA · Blackwell
Best AI Models for NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition (96 GB)
Runs everything up to 120B MoE well.
300 W variant of the 96 GB RTX PRO 6000 — lower clocks than the 600 W edition, but two fit inside a 600 W budget for multi-GPU rigs.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | Q4_K_M · 21.7 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed | Coding Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | Q4_K_M · 21.7 GB | |
| 3 | Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 213 t/s | 213 tok/s | Q4_K_M · 22.0 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | Q4_K_M · 21.7 GB | |
| 3 | Qwen3.5 27BAlibaba Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed | Math Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 213 t/s | 213 tok/s | Q4_K_M · 22.0 GB | |
| 4 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | overall | Q4_K_M · 21.7 GB |
| 5 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 357 t/s | 357 tok/s | Q4_K_M · 3.4 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 282 t/s | 282 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 4 | Q4_K_M · 19.3 GB · 244 t/s | 244 tok/s | Q4_K_M · 19.3 GB | |
| 5 | Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB · 227 t/s | 227 tok/s | Q4_K_M · 19.4 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 57.1 t/s | 57.1 tok/s | Q4_K_M · 21.7 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 250 t/s | 250 tok/s | Q4_K_M · 16.5 GB | |
| 5 | JEV 27B VLautotrust Q4_K_M · 17.9 GB · 66.9 t/s | 66.9 tok/s | Q4_K_M · 17.9 GB |
The RTX PRO 6000 Blackwell Max-Q Workstation Edition runs everything up to 120B MoE well. Blackwell generation, released 2025.
RTX PRO 6000 Blackwell Max-Q Workstation Edition strengths and weaknesses
Strengths
- Runs 120B MoE models: GPT OSS 120B at 202 tok/s in 70.8 GB
- Room for long context: Llama 3.3 70B Instruct at Q4 uses 51.3 GB, leaving 44.7 GB free
- Reads long prompts fast: A 4K-token prompt on Qwen3.8 27B starts answering in 1.2 s
- FP8 and NVFP4 run natively: Blackwell takes FP8 and NVFP4 checkpoints in vLLM as published
Output speed depends on memory bandwidth (1792 GB/s).
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB183tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB123tok/s
- 27–31BQwen3.8 27B · Q4_K_M · 17.9 GB66.9tok/s
- 35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB213tok/s
- 70BLlama 3.3 70B Instruct · Q4_K_M · 51.3 GB25.0tok/s
- 120B MoEGPT OSS 120B · Q4_K_M · 70.8 GB202tok/s
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 1270 models on the RTX PRO 6000 Blackwell Max-Q Workstation Edition
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
| Q4_K_M | 0.8 GB | 1941.3 t/s | 8K | S100, Runs great | ||
| Q4_K_M | 6.2 GB | 216.9 t/s | 8K | S100, Runs great | ||
| Q4_K_M | 24.1 GB | 52.3 t/s | 524K | A81, Runs well | ||
| Q4_K_M | 10.4 GB | 152.7 t/s | 33K | S97, Runs great | ||
Phi 4 Mini Flash ReasoningChatMathCodeReasoning | Q4_K_M | 5.3 GB | 394.8 t/s | 262K | S100, Runs great | |
Mistral 7B v0.2Chat | Q4_K_M | 6.8 GB | 237.2 t/s | 33K | S100, Runs great | |
TinyLLama V0Chat | Q4_K_M | 0.0 GB | 116480.0 t/s | 2K | S100, Runs great | |
Olmo 3 7B ThinkChat | Q4_K_M | 8.4 GB | 202.6 t/s | 66K | S100, Runs great | |
Opt 1.3BChat | FP16 | 2.9 GB | 407.3 t/s | 2K | S100, Runs great | |
| Q4_K_M | 9.8 GB | 119.0 t/s | 2K | S95, Runs great | ||
Gpt2 XlChat | Q4_K_M | 1.1 GB | 1098.9 t/s | 1K | S100, Runs great | |
CodeLlama 70B Instruct HFChatCode | Q4_K_M | 43.0 GB | 27.5 t/s | 4K | A68, Runs well | |
Pythia 1.4BChat | Q4_K_M | 1 GB | 1164.8 t/s | 2K | S100, Runs great | |
Zephyr 7B BetaChat | Q4_K_M | 6.8 GB | 237.2 t/s | 33K | S100, Runs great | |
SmolLM3 3B BaseChat | Q4_K_M | 3.4 GB | 506.4 t/s | 66K | S100, Runs great |
Rent NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition in the cloud
Hourly rate on demand for one RTX PRO 6000 Blackwell Max-Q Workstation Edition.
Vast.aiMarketplace
from$1.34/hr
Buying one ($8,565) costs as much as ~6,400 hours of rental, about 27 months at 8 hours a day.
Cost per million tokens
Output-token cost on an RTX PRO 6000 Blackwell Max-Q Workstation Edition rented at $1.34/hr, one request at a time. Batch several users on the same card and the cost per token drops several-fold.
| Model | Quant | Speed | Per 1M output |
|---|---|---|---|
| NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 | 244 tok/s | $1.53$0.0089 input | |
| GLM 4.7 Flash | 196 tok/s | $1.90$0.017 input | |
| Qwen3.8 27B | 67 tok/s | $5.56$0.10 input | |
| Muse Glimmer 30B | 64 tok/s | $5.86$0.11 input | |
| Gemma 4 31B IT | 57 tok/s | $6.51$0.12 input |
Specs
- Architecture
- Blackwell
- VRAM
- 96 GB GDDR7
- Memory bandwidth
- 1792.0 GB/s
- FP16 (dense)
- 438.90 TFLOPS
- CUDA cores
- 24,064
- Tensor cores
- 752
- Compute capability
- 12.0
- TDP
- 300 W
- Released
- 2025-04-01
- MSRP
- $8,565
RTX PRO 6000 Blackwell Max-Q Workstation Edition FAQ
What is the largest model the RTX PRO 6000 Blackwell Max-Q Workstation Edition can run?
Mistral Medium 3.5 128B (127.7B) is the largest at 4-bit or better: at Q4_K_M it uses 82.8 GB of 96 GB, and runs at about 15.0 tok/s.
Can the RTX PRO 6000 Blackwell Max-Q Workstation Edition run Qwen3.8 27B?
Yes. Qwen3.8 27B fits at Q4_K_M, using 17.9 GB of 96 GB, and it is the best model that runs well on this card.
How many tokens per second does the RTX PRO 6000 Blackwell Max-Q Workstation Edition give?
About 66.9 tok/s on Qwen3.8 27B at Q4_K_M, with the first token after 1.2 s on a 4K-token prompt.
What power supply does the RTX PRO 6000 Blackwell Max-Q Workstation Edition need?
Plan a 600 W PSU (2× the 300 W TDP).
Is 96 GB enough for coding and agents?
Yes for coding: Qwen3.8 27B, the best of the 14 coding models that fit, runs at about 67 tok/s.