MacBook Pro M4 Max 48GB经济之选
48 GB Unified (+16)546 GB/s (+34)
C
Raises estimated decode speed by about 31%.87.8 tok/s 解码
Raises estimated decode speed by about 31%.
~$2,499 MSRP
Can Mistral 7B Instruct v0.2 run on Radeon Pro W6800 32GB?
YES — Runs Great
C47Usable
Estimated from fit model
Mistral 7B Instruct v0.2 needs ~9.2 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~67 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 9.2 GB, 67.1 tok/s, Runs well
9.2 GB required32.0 GB available
29% VRAM used
Fit status
Runs well
Decode
67.1 tok/s
TTFT
2883 ms
Safe context
461K
Memory
9.2 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsMistral 7B Instruct v0.2 on Radeon Pro W6800 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 67.1 tok/s decode · 2.9s TTFT (warm) · 168 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 | C | Runs well | 67.1 tok/s | 1573 ms | 461K |
| Coding | C | Runs well | 67.1 tok/s | 2883 ms | 461K |
| Agentic Coding | C | Runs well | 67.1 tok/s | 4194 ms | 461K |
| Reasoning | C | Runs well | 67.1 tok/s | 3407 ms | 461K |
| RAG | C | Runs well | 67.1 tok/s | 5242 ms | 461K |
Quantization options
How Mistral 7B Instruct v0.2 (7B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C43 |
Q3_K_S | 3 | 3.4 GB | Low | C43 |
NVFP4 | 4 | 3.9 GB | Medium | C44 |
Q4_K_M | 4 | 4.3 GB | Medium | C44 |
Q5_K_M | 5 | 5.0 GB | High | C44 |
Q6_K | 6 | 5.7 GB | High | C44 |
Q8_0 | 8 | 7.5 GB | Very High | C45 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C48 |
Get started
Copy-paste commands to run Mistral 7B Instruct v0.2 on your machine.
Run
lms load hf-thebloke--mistral-7b-instruct-v0-2-gguf && lms server start升级选项
能流畅运行 Mistral 7B Instruct v0.2 的硬件
Frequently asked questions
Can Radeon Pro W6800 32GB run Mistral 7B Instruct v0.2?
Yes, Radeon Pro W6800 32GB can run Mistral 7B Instruct v0.2 with a C grade (Runs well). Expected decode speed: 67.1 tok/s.
How much VRAM does Mistral 7B Instruct v0.2 need?
Mistral 7B Instruct v0.2 (7B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Mistral 7B Instruct v0.2?
The recommended quantization for Mistral 7B Instruct v0.2 is Q4_K_M, which balances quality and memory efficiency.
What speed will Mistral 7B Instruct v0.2 run at on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Mistral 7B Instruct v0.2 achieves approximately 67.1 tokens per second decode speed with a time-to-first-token of 2883ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run Mistral 7B Instruct v0.2 for coding?
For coding workloads, Mistral 7B Instruct v0.2 on Radeon Pro W6800 32GB receives a C grade with 67.1 tok/s and 461K context.
What context window can Mistral 7B Instruct v0.2 use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Mistral 7B Instruct v0.2 can safely use up to 461K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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