Radeon Pro W7900 48GBGünstige Wahl
864 GB/s (+591)
C
Raises estimated decode speed by about 62%.34.8 tok/s Dekodierung
Raises estimated decode speed by about 62%.
ca. $3,999 MSRP
Can Mistral Small 24B Instruct 2501 run on MacBook Pro M4 Pro 64GB?
YES — Runs Great
C49Usable
Estimated — low-sample bucket· few comparable runs
Mistral Small 24B Instruct 2501 needs ~25.3 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~22 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 25.3 GB, 21.5 tok/s, Runs well
25.3 GB required46.1 GB available
55% VRAM used
Fit status
Runs well
Decode
21.5 tok/s
TTFT
8989 ms
Safe context
134K
Memory
25.3 GB / 46.1 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom6.9 GB
See how fast it feels
See how fast it feelsMistral Small 24B Instruct 2501 on MacBook Pro M4 Pro 64GB
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: 21.5 tok/s decode · 9.0s TTFT (warm) · 54 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 21.5 tok/s | 4903 ms | 134K |
| Coding | C | Runs well | 21.5 tok/s | 8989 ms | 134K |
| Agentic Coding | C | Runs well | 21.5 tok/s | 13075 ms | 134K |
| Reasoning | C | Runs well | 21.5 tok/s | 10623 ms | 134K |
| RAG | C | Runs well | 21.5 tok/s | 16343 ms | 134K |
Quantization options
How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C43 |
Q3_K_S | 3 | 11.8 GB | Low | C44 |
NVFP4 | 4 | 13.4 GB | Medium | C44 |
Q4_K_M | 4 | 14.6 GB | Medium | C45 |
Q5_K_M | 5 | 17.3 GB | High | C46 |
Q6_K | 6 | 19.7 GB | High | C46 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | C48 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Get started
Copy-paste commands to run Mistral Small 24B Instruct 2501 on your machine.
Run
lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server startUpgrade-Optionen
Hardware, die Mistral Small 24B Instruct 2501 gut ausführt
Frequently asked questions
Can MacBook Pro M4 Pro 64GB run Mistral Small 24B Instruct 2501?
Yes, MacBook Pro M4 Pro 64GB can run Mistral Small 24B Instruct 2501 with a C grade (Runs well). Expected decode speed: 21.5 tok/s.
How much VRAM does Mistral Small 24B Instruct 2501 need?
Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Mistral Small 24B Instruct 2501?
The recommended quantization for Mistral Small 24B Instruct 2501 is Q4_K_M, which balances quality and memory efficiency.
What speed will Mistral Small 24B Instruct 2501 run at on MacBook Pro M4 Pro 64GB?
On MacBook Pro M4 Pro 64GB, Mistral Small 24B Instruct 2501 achieves approximately 21.5 tokens per second decode speed with a time-to-first-token of 8989ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 64GB run Mistral Small 24B Instruct 2501 for coding?
For coding workloads, Mistral Small 24B Instruct 2501 on MacBook Pro M4 Pro 64GB receives a C grade with 21.5 tok/s and 134K context.
What context window can Mistral Small 24B Instruct 2501 use on MacBook Pro M4 Pro 64GB?
On MacBook Pro M4 Pro 64GB, Mistral Small 24B Instruct 2501 can safely use up to 134K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Pro 64GB as fast as VRAM for Mistral Small 24B Instruct 2501?
Not always. MacBook Pro M4 Pro 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.
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