NVIDIA H200 141GB经济之选141 GB VRAM (+45)4800 GB/s (+3008)
A
Raises estimated decode speed by about 280%.97.4 tok/s 解码
Raises estimated decode speed by about 280%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Can Mixtral 8x22B run on RTX PRO 6000 Blackwell Workstation Edition 96GB?
YES — With Offload
B63Good
Estimated from fit model
Mixtral 8x22B needs ~99.9 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~26 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 99.9 GB, 25.6 tok/s, Runs with offload (needs ~3.4 GB host RAM)
99.9 GB required96.0 GB available
104% VRAM needed
3.9 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~3.4 GB host RAM)
Decode
25.6 tok/s
TTFT
7554 ms
Safe context
4K
Memory
99.9 GB / 96.0 GB
Memory breakdown
Weights86.0 GB
KV Cache3.4 GB
Runtime0.9 GB
Headroom9.6 GB
See how fast it feels
See how fast it feelsMixtral 8x22B on RTX PRO 6000 Blackwell Workstation Edition 96GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 25.6 tok/s decode · 7.6s TTFT (warm) · 64 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload | 24.5 tok/s | 4309 ms | 4K |
| Coding | B | Runs with offload (needs ~3.4 GB host RAM) | 25.6 tok/s | 7554 ms | 4K |
| Agentic Coding | C | Runs with offload (needs ~6.1 GB host RAM) | 23.9 tok/s | 11768 ms | 4K |
| Reasoning | B | Runs with offload (needs ~3.4 GB host RAM) | 25.6 tok/s | 8927 ms | 4K |
| RAG | C | Runs with offload (needs ~6.1 GB host RAM) | 23.9 tok/s | 14710 ms | 4K |
Quantization options
How Mixtral 8x22B (141B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 55.0 GB | Low | B61 |
Q3_K_SBest for your GPU | 3 | 69.1 GB | Low | B61 |
NVFP4 | 4 | 79.0 GB | Medium | F0 |
Q4_K_M | 4 | 86.0 GB | Medium | F0 |
Q5_K_M | 5 | 101.5 GB | High | F0 |
Q6_K | 6 | 115.6 GB | High | F0 |
Q8_0 | 8 | 150.9 GB | Very High | F0 |
F16 | 16 | 289.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run Mixtral 8x22B on your machine.
Run
ollama run mixtral:8x22b升级选项
能流畅运行 Mixtral 8x22B 的硬件
NVIDIA H200 141GB经济之选NVIDIA H200 PCIe 141GB最佳性价比141 GB VRAM (+45)4800 GB/s (+3008)
A
Raises estimated decode speed by about 280%.97.4 tok/s 解码
Raises estimated decode speed by about 280%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA 升级180 GB VRAM (+84)8000 GB/s (+6208)
B
Raises estimated decode speed by about 534%.162.4 tok/s 解码
Raises estimated decode speed by about 534%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Frequently asked questions
Can RTX PRO 6000 Blackwell Workstation Edition 96GB run Mixtral 8x22B?
Yes, RTX PRO 6000 Blackwell Workstation Edition 96GB can run Mixtral 8x22B with a B grade (Runs with offload (needs ~3.4 GB host RAM)). Expected decode speed: 25.6 tok/s.
How much VRAM does Mixtral 8x22B need?
Mixtral 8x22B (141B parameters) requires approximately 99.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Mixtral 8x22B?
The recommended quantization for Mixtral 8x22B is Q4_K_M, which balances quality and memory efficiency.
What speed will Mixtral 8x22B run at on RTX PRO 6000 Blackwell Workstation Edition 96GB?
On RTX PRO 6000 Blackwell Workstation Edition 96GB, Mixtral 8x22B achieves approximately 25.6 tokens per second decode speed with a time-to-first-token of 7554ms using Q4_K_M quantization.
Can RTX PRO 6000 Blackwell Workstation Edition 96GB run Mixtral 8x22B for coding?
For coding workloads, Mixtral 8x22B on RTX PRO 6000 Blackwell Workstation Edition 96GB receives a B grade with 25.6 tok/s and 4K context.
What context window can Mixtral 8x22B use on RTX PRO 6000 Blackwell Workstation Edition 96GB?
On RTX PRO 6000 Blackwell Workstation Edition 96GB, Mixtral 8x22B can safely use up to 4K tokens of context. The model's official context limit is 66K, but available memory constrains the safe maximum.
What should I upgrade first if Mixtral 8x22B feels slow on RTX PRO 6000 Blackwell Workstation Edition 96GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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