Mamba Codestral 7B v0.1 needs ~12.9 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~23 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
23.3 tok/s
TTFT
8320 ms
Safe context
663K
Memory
12.9 GB / 46.1 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 | 23.3 tok/s | 4538 ms | 663K |
| Coding | C | Runs well | 23.3 tok/s | 8320 ms | 663K |
| Agentic Coding | C | Runs well | 23.3 tok/s | 12102 ms | 663K |
| Reasoning | C | Runs well | 23.3 tok/s | 9833 ms | 663K |
| RAG | C | Runs well | 23.3 tok/s | 15127 ms | 663K |
How Mamba Codestral 7B v0.1 (7B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C41 |
Q3_K_S | 3 | 3.4 GB | Low | C41 |
NVFP4 | 4 | 3.9 GB | Medium | C41 |
Q4_K_M | 4 | 4.3 GB | Medium | C41 |
Q5_K_M | 5 | 5.0 GB | High | C41 |
Q6_K | 6 | 5.7 GB | High | C42 |
Q8_0 | 8 | 7.5 GB | Very High | C42 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C44 |
Copy-paste commands to run Mamba Codestral 7B v0.1 on your machine.
Run
lms load hf-gabriellarson--mamba-codestral-7b-v0-1-gguf && lms server startアップグレードオプション
Raises estimated decode speed by about 297%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Raises estimated decode speed by about 177%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Raises estimated decode speed by about 321%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Yes, Mac mini M4 64GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 23.3 tok/s.
Mamba Codestral 7B v0.1 (7B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Mamba Codestral 7B v0.1 is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 64GB, Mamba Codestral 7B v0.1 achieves approximately 23.3 tokens per second decode speed with a time-to-first-token of 8320ms using Q4_K_M quantization.
For coding workloads, Mamba Codestral 7B v0.1 on Mac mini M4 64GB receives a C grade with 23.3 tok/s and 663K context.
On Mac mini M4 64GB, Mamba Codestral 7B v0.1 can safely use up to 663K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac mini M4 64GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-gabriellarson--mamba-codestral-7b-v0-1-gguf-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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