Mistral 7B Instruct v0.3 needs ~19.8 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~98 tok/s.
Operating mode
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
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
1.4M
Memory
19.8 GB / 92.2 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 98.0 tok/s | 1078 ms | 1.4M |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 1.4M |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 1.4M |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 1.4M |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 1.4M |
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | D39 |
Q3_K_S | 3 | 3.4 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run Mistral 7B Instruct v0.3 on your machine.
Run
lms load hf-sanctumai--mistral-7b-instruct-v0-3-gguf && lms server startYes, Mac Studio M1 Ultra 128GB can run Mistral 7B Instruct v0.3 with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
Mistral 7B Instruct v0.3 (7B parameters) requires approximately 19.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral 7B Instruct v0.3 is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M1 Ultra 128GB, Mistral 7B Instruct v0.3 achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, Mistral 7B Instruct v0.3 on Mac Studio M1 Ultra 128GB receives a C grade with 98.0 tok/s and 1.4M context.
On Mac Studio M1 Ultra 128GB, Mistral 7B Instruct v0.3 can safely use up to 1.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-sanctumai--mistral-7b-instruct-v0-3-gguf-on-m1-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| D39 |
Q4_K_M | 4 | 4.3 GB | Medium | D39 |
Q5_K_M | 5 | 5.0 GB | High | D39 |
Q6_K | 6 | 5.7 GB | High | D39 |
Q8_0 | 8 | 7.5 GB | Very High | D39 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C40 |
Not always. Mac Studio M1 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.