Canonical Rankings

Best Macs for this model

Devstral Small 2 24B ranked across the Mac lineup at the best practical quantization, using the best available runtime evidence. Model picker is focused on current-market choices.

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29 ranked MacsUse the strongest current runtime evidence for each row.28 historical models hiddenBaselinesStatic paths cover only canonical model pages; sort and quantization stay as query state.

Rank	Mac	Score	Quant	Tok/s	Runtime	Fits	Headroom	Context	Evidence	Price	Why it ranks here
1	Mac Studio M3 Ultra 256GB	486	8bit	47.0 tok/s Fastest evidence path: 8bit · 47.0 tok/s · MLX · Estimated	MLX	Fits	231.9 GB	262k	Estimated	$7,499	8bit is the current best practical quantization. 47.0 tok/s is estimated from nearby benchmark coverage. 231.9 GB headroom remains at this quantization.
2	Mac Pro M2 Ultra 192GB	327	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	167.9 GB	262k	Estimated	$6,999	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 167.9 GB headroom remains at this quantization.
3	Mac Studio M4 Max 128GB	263	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	103.9 GB	262k	Estimated	$4,499	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 103.9 GB headroom remains at this quantization.
4	MacBook Pro M5 Max 128GB 16-inch	263	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	103.9 GB	262k	Estimated	$5,399	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 103.9 GB headroom remains at this quantization.
5	MacBook Pro M4 Max 128GB 16-inch	263	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	103.9 GB	262k	Estimated	$5,999	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 103.9 GB headroom remains at this quantization.
6	Mac Studio M3 Ultra 96GB	231	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	71.9 GB	262k	Estimated	$3,999	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 71.9 GB headroom remains at this quantization.
7	Mac Studio M4 Max 64GB	199	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	39.9 GB	207k	Estimated	$2,999	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 39.9 GB headroom remains at this quantization.
8	MacBook Pro M4 Max 64GB 16-inch	199	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	39.9 GB	207k	Estimated	$4,499	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 39.9 GB headroom remains at this quantization.
9	Mac Mini M4 Pro 48GB	183	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	23.9 GB	118k	Estimated	$1,599	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 23.9 GB headroom remains at this quantization.
10	MacBook Pro M4 Pro 48GB 14-inch	183	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	23.9 GB	118k	Estimated	$2,499	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 23.9 GB headroom remains at this quantization.
11	Mac Studio M4 Max 48GB	183	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	23.9 GB	118k	Estimated	$2,499	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 23.9 GB headroom remains at this quantization.
12	MacBook Pro M4 Pro 48GB 16-inch	183	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	23.9 GB	118k	Estimated	$2,999	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 23.9 GB headroom remains at this quantization.
13	MacBook Pro M4 Max 48GB 14-inch	183	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	23.9 GB	118k	Estimated	$3,499	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 23.9 GB headroom remains at this quantization.
14	MacBook Pro M4 Max 48GB 16-inch	183	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	23.9 GB	118k	Estimated	$3,999	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 23.9 GB headroom remains at this quantization.
15	Mac Studio M4 Max 36GB	171	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	11.9 GB	51k	Estimated	$1,999	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 11.9 GB headroom remains at this quantization.
16	MacBook Pro M4 Max 36GB 14-inch	171	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	11.9 GB	51k	Estimated	$2,999	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 11.9 GB headroom remains at this quantization.
17	MacBook Pro M4 Max 36GB 16-inch	171	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	11.9 GB	51k	Estimated	$3,499	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 11.9 GB headroom remains at this quantization.
18	Mac Mini M4 32GB	167	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	7.9 GB	28k	Estimated	$799	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 7.9 GB headroom remains at this quantization.
19	MacBook Air M4 32GB 13-inch	167	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	7.9 GB	28k	Estimated	$1,499	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 7.9 GB headroom remains at this quantization.
20	MacBook Air M4 32GB 15-inch	167	8bit	23.4 tok/s Fastest evidence path: 8bit · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	7.9 GB	28k	Estimated	$1,699	8bit is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 7.9 GB headroom remains at this quantization.
21	Mac Mini M4 24GB	157	Q6_K	23.4 tok/s Fastest evidence path: Q6_K · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	3.9 GB	10k	Estimated	$599	Q6_K is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 3.9 GB headroom remains at this quantization.
22	MacBook Air M4 24GB 13-inch	157	Q6_K	23.4 tok/s Fastest evidence path: Q6_K · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	3.9 GB	10k	Estimated	$1,299	Q6_K is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 3.9 GB headroom remains at this quantization.
23	Mac Mini M4 Pro 24GB	157	Q6_K	23.4 tok/s Fastest evidence path: Q6_K · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	3.9 GB	10k	Estimated	$1,399	Q6_K is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 3.9 GB headroom remains at this quantization.
24	MacBook Air M4 24GB 15-inch	157	Q6_K	23.4 tok/s Fastest evidence path: Q6_K · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	3.9 GB	10k	Estimated	$1,499	Q6_K is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 3.9 GB headroom remains at this quantization.
25	MacBook Pro M4 Pro 24GB 14-inch	157	Q6_K	23.4 tok/s Fastest evidence path: Q6_K · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	3.9 GB	10k	Estimated	$1,999	Q6_K is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 3.9 GB headroom remains at this quantization.
26	MacBook Pro M4 Pro 24GB 16-inch	157	Q6_K	23.4 tok/s Fastest evidence path: Q6_K · 23.4 tok/s · llama.cpp · Estimated	llama.cpp	Fits	3.9 GB	10k	Estimated	$2,499	Q6_K is the current best practical quantization. 23.4 tok/s is estimated from nearby benchmark coverage. 3.9 GB headroom remains at this quantization.
27	Mac Mini M4 16GB	57	q4.1bit	0.1 tok/s Fastest evidence path: Q4_0 · 3.4 tok/s · llama.cpp · Community row	llama.cpp	Fits	2.8 GB	11k	Estimated	$499	q4.1bit is the current best practical quantization. 0.1 tok/s is estimated from nearby benchmark coverage. 2.8 GB headroom remains at this quantization.
28	MacBook Air M4 16GB 13-inch	57	q4.1bit	0.1 tok/s Fastest evidence path: Q4_0 · 3.4 tok/s · llama.cpp · Community row	llama.cpp	Fits	2.8 GB	11k	Estimated	$1,099	q4.1bit is the current best practical quantization. 0.1 tok/s is estimated from nearby benchmark coverage. 2.8 GB headroom remains at this quantization.
29	MacBook Air M4 16GB 15-inch	57	q4.1bit	0.1 tok/s Fastest evidence path: Q4_0 · 3.4 tok/s · llama.cpp · Community row	llama.cpp	Fits	2.8 GB	11k	Estimated	$1,299	q4.1bit is the current best practical quantization. 0.1 tok/s is estimated from nearby benchmark coverage. 2.8 GB headroom remains at this quantization.

Devstral Small 2 24B Apple Silicon benchmark and best Macs

Start with the ranked Mac table above, then audit tokens per second, RAM fit, quantization, runtimes, and source links before trusting the model on your Mac.

Quantizations observed: 4bit, Q4_K - Medium, Q8, 8bit, Q4_0, Q4_1

8Benchmark rows

3Chip tiers covered

47.0Fastest avg tok/s (M3 Ultra (256 GB))

13.4 GBMinimum RAM observed

Quick take

Fastest published result is 47.0 tok/s on M3 Ultra (256 GB) at 4bit. Smallest published fit is 13.4 GB on M3 Ultra (256 GB). Published runtimes include llama.cpp, MLX. Start with Rankings for the decision, then use the raw rows below to audit the evidence.

Based on 8 external benchmarks; no lab runs yet.

Published runtimes: llama.cpp, MLX.

Need the best Mac for this model? Use Buy Need a setup-first answer? Use Run Checking whether it fits? Use Fit Browse Macs by exact hardware Need the full audit trail? Use Bench Comparing against rented GPUs? Use AI Datacenter Index

Best Mac shortlist

These are ranked by the fastest published Devstral Small 2 24B result available for each Mac's chip family. Use them as the search answer, then open the machine page before buying.

Fastest published Mac-family result

Mac Studio M3 Ultra 96GB maps to 47.0 tok/s from the strongest published chip-family row for Devstral Small 2 24B.

Open Mac Studio M3 Ultra 96GB · Audit evidence

Shortlist rank 2

Mac Studio M3 Ultra 256GB maps to 47.0 tok/s from the strongest published chip-family row for Devstral Small 2 24B.

Open Mac Studio M3 Ultra 256GB · Audit evidence

Shortlist rank 3

Mac Mini M4 16GB maps to 3.4 tok/s from the strongest published chip-family row for Devstral Small 2 24B.

Open Mac Mini M4 16GB · Audit evidence

Model search answers

How fast is Devstral Small 2 24B on Mac?

Devstral Small 2 24B currently has a fastest published Mac result of 47.0 tok/s on M3 Ultra (256 GB) at 4bit. Check the raw rows on this page before comparing that number to a different runtime, quantization, or context length.

What is the best Mac for Devstral Small 2 24B?

Mac Studio M3 Ultra 96GB is the fastest published Mac-family answer for Devstral Small 2 24B on this page right now, at 47.0 tok/s from its chip family. Treat that as a published benchmark starting point, not a universal buying recommendation.

Does Devstral Small 2 24B fit on Apple Silicon?

Devstral Small 2 24B has a smallest published fit of 13.4 GB on M3 Ultra (256 GB). More memory may still be required for longer context, different quantization, or parallel workloads.

Catalog record

24BTotal params

DenseActive params

262,144Context window

2025-11-28Release date

What this model is, and what Apple Silicon users are actually seeing

Official model cards tell you what the model is for and which software stacks it targets. Field reality below shows how much Apple Silicon evidence we have so far.

Official brief

The Devstral Small 2 Instruct model offers the following capabilities: Agentic Coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents.

Official source · Raw model card

agentscodingvisual-understanding

Runtime support mentioned

llama.cppOllamaLM StudiovLLMSGLangTransformersOpenHandsClaude CodeClineKilo CodeMistral Vibe

Official specs

Total parameters: 24B.
Context: 256k tokens.
Modalities: Text and image input, text output.
License: Apache 2.0.

Official takeaways

Agentic Coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents.
Lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use.
Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
Context Window: A 256k context window.

Deployment notes

You can use Devstral 2 either through our API or by running locally.
You can now also run Devstral using these (alphabetical ordered) frameworks: llama.cpp: To use community ones such as Unsloth's or Bartowski's make sure to use changes from this PR.
If you notice subpar performance with local serving, please submit issues to the relevant framework so that it can be fixed and in the meantime we advise to use the Mistral AI API.

Apple Silicon note: The Devstral Small 2 Instruct model offers the following capabilities: Agentic Coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents. Lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use. Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes. Context Window: A 256k context window.

Official model cards describe intent, capabilities, and supported stacks. They do not prove Apple Silicon speed by themselves.

Field reality on Apple Silicon

Devstral Small 2 24B: 7 Apple Silicon field reports; best reported generation ~47 tok/s; seen on Mac Studio M3 ULTRA 256GB, M1 ULTRA 64GB, Mac Mini M4 16GB; via MLX, llama.cpp.

8Benchmark rows

7Field reports

8Practitioner signals

Sparse BenchmarksEvidence status

What practitioners keep saying

The benchmark summary flags Devstral Small 2 24B Q4_1 at about 17.5 seconds to first token and only about 0.054 tok/s, which is well below a usable interactive threshold.
The same sweep lists Devstral Small 2 24B Q4_K_M at about 65.3 seconds to first token and only about 0.020 tok/s, reinforcing that 16GB buyers should treat this model as non-viable rather than merely slow.
The report is based on actual code-assistance work rather than benchmark screenshots.

Apple Silicon field sources

Manojb Hugging Face model cards
2026-03-26 · Mac Mini M4 16GB · llama.cpp
A broad 16GB Mac Mini M4 sweep says Devstral Small 2 24B is effectively a trap tier on constrained Apple Silicon: it may load in aggressive GGUFs, but practical latency collapses.
r/LocalLLaMA
2026-03-19 · 16GB GPU local workstation
Practitioners are calling Devstral Small 2 materially better for real coding than the popular consensus suggested.
r/LocalLLaMA
2026-03-04 · Mac Studio M3 Ultra 256GB · MLX
A standardized M3 Ultra eval sharpens the Devstral Small 2 story on Mac: strong code model, weak tool-calling defaults.
r/LocalLLaMA
2026-02-02 · Local agentic coding workstation · OpenCode
Practitioner discussion is framing Devstral Small 2 as more time-efficient for local agentic coding than GLM-4.7-Flash despite lower raw tok/s.
r/LocalLLaMA
2025-12-13 · M1 Ultra 64GB · LM Studio (llama.cpp), LM Studio (MLX)
The same M1 Ultra macOS report measures Devstral Small 2 GGUF Q4_K_M through LM Studio's llama.cpp path at usable coding speed.

Runtime mentions in the field

llama.cppLM StudioMLXOpenCode

Hardware mentioned in reports

16GB64GBM1 UltraM3 UltraM4MacMac MiniMac Studio

What would improve confidence

Reproduce Field Performance Signal
Upgrade To First Party Measurement

Current published coverage

Published chip coverage includes M3 Ultra (256 GB), M1 Ultra (64 GB), M4 (16 GB). Fastest published row is 47.0 tok/s on M3 Ultra (256 GB) at 4bit. Lowest published RAM requirement is 13.4 GB on M3 Ultra (256 GB).

M3 Ultra (256 GB)M1 Ultra (64 GB)M4 (16 GB)

Related Devstral Small 2 models with published pages: Devstral Small 1.1

Standardized eval scorecards for Devstral Small 2 24B

These are fixed-machine model scorecards from a single Apple Silicon setup. They help explain whether a model is merely fast or actually good at tools, coding, reasoning, and general tasks. They do not replace the main Mac ranking above.

4bit · vLLM-MLX

Mac Studio M3 Ultra 256GB · Avg 62%

17%Tools

90%Coding

70%Reasoning

70%General

Speed and memory

Long decode: 47.2 tok/s
Short decode: 22.5 tok/s
Cold TTFT: 0.291 s
Active RAM: 13.4 GB

Strong coding score, but tool calling is poor in this standardized setup.

vLLM-MLX SCORECARD.md · discussion · 2026-03-04

Raw benchmark rows for Devstral Small 2 24B

Rows stay below the ranking because this page is answer-first. Use them to inspect exact chips, quantizations, runtimes, and sources.

Chip	Quant	RAM req.	Context	Avg tok/s	Prompt tok/s	Runtime	Source
M3 Ultra (256 GB)	4bit	13.4 GB	—	47.0 tok/s	—	MLX	ref
M1 Ultra (64 GB)	4bit	—	—	29.7 tok/s	—	MLX	ref
M1 Ultra (64 GB)	Q4_K - Medium	—	—	25.3 tok/s	—	llama.cpp	ref
M1 Ultra (64 GB)	Q8	—	—	23.4 tok/s	—	llama.cpp	ref
M1 Ultra (64 GB)	8bit	—	—	22.3 tok/s	—	MLX	ref
M4 (16 GB)	Q4_0	—	—	3.4 tok/s	—	llama.cpp	ref
M4 (16 GB)	Q4_1	—	—	0.1 tok/s	—	llama.cpp	ref
M4 (16 GB)	Q4_K - Medium	—	—	0.0 tok/s	—	llama.cpp	ref

Chips with published results for Devstral Small 2 24B

M3 Ultra (256 GB)M1 Ultra (64 GB)M4 (16 GB)

Data

benchmarks.json — full dataset · models.json — model summaries · benchmarks.csv — CSV export

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