NVIDIA A100 80GB经济之选80 GB VRAM (+16)2039 GB/s (+1439)
B
Raises estimated decode speed by about 266%.123.2 tok/s 解码
Raises estimated decode speed by about 266%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
Can Mixtral 8x7B run on NVIDIA A16 64GB?
YES — Runs Great
B67Good
Estimated from fit model
Mixtral 8x7B needs ~38.2 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~34 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 38.2 GB, 33.7 tok/s, Runs well
38.2 GB required64.0 GB available
60% VRAM used
Fit status
Runs well
Decode
33.7 tok/s
TTFT
5751 ms
Safe context
33K
Memory
38.2 GB / 64.0 GB
Memory breakdown
Weights28.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsMixtral 8x7B on NVIDIA A16 64GB
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: 33.7 tok/s decode · 5.8s TTFT (warm) · 84 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 33.7 tok/s | 3137 ms | 33K |
| Coding | B | Runs well | 33.7 tok/s | 5751 ms | 33K |
| Agentic Coding | B | Runs well | 33.7 tok/s | 8365 ms | 33K |
| Reasoning | B | Runs well | 33.7 tok/s | 6797 ms | 33K |
| RAG | B | Runs well | 33.7 tok/s | 10456 ms | 33K |
Quantization options
How Mixtral 8x7B (47B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.3 GB | Low | B59 |
Q3_K_S | 3 | 23.0 GB | Low | B60 |
NVFP4 | 4 | 26.3 GB | Medium | B61 |
Q4_K_M | 4 | 28.7 GB | Medium | B61 |
Q5_K_M | 5 | 33.8 GB | High | B63 |
Q6_K | 6 | 38.5 GB | High | B63 |
Q8_0Best for your GPU | 8 | 50.3 GB | Very High | B63 |
F16 | 16 | 96.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run Mixtral 8x7B on your machine.
Run
ollama run mixtral升级选项
能流畅运行 Mixtral 8x7B 的硬件
NVIDIA A100 80GB经济之选NVIDIA A800 80GB最佳性价比80 GB VRAM (+16)1935 GB/s (+1335)
B
Raises estimated decode speed by about 222%.108.6 tok/s 解码
Raises estimated decode speed by about 222%.
Adds memory headroom for longer context windows and future model growth.
~$15,000 MSRP
NVIDIA H800 80GBNVIDIA 升级80 GB VRAM (+16)3000 GB/s (+2400)
B
Raises estimated decode speed by about 419%.174.8 tok/s 解码
Raises estimated decode speed by about 419%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Frequently asked questions
Can NVIDIA A16 64GB run Mixtral 8x7B?
Yes, NVIDIA A16 64GB can run Mixtral 8x7B with a B grade (Runs well). Expected decode speed: 33.7 tok/s.
How much VRAM does Mixtral 8x7B need?
Mixtral 8x7B (47B parameters) requires approximately 38.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Mixtral 8x7B?
The recommended quantization for Mixtral 8x7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Mixtral 8x7B run at on NVIDIA A16 64GB?
On NVIDIA A16 64GB, Mixtral 8x7B achieves approximately 33.7 tokens per second decode speed with a time-to-first-token of 5751ms using Q4_K_M quantization.
Can NVIDIA A16 64GB run Mixtral 8x7B for coding?
For coding workloads, Mixtral 8x7B on NVIDIA A16 64GB receives a B grade with 33.7 tok/s and 33K context.
What context window can Mixtral 8x7B use on NVIDIA A16 64GB?
On NVIDIA A16 64GB, Mixtral 8x7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
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