Canonical Rankings

Best Macs for this model

Qwen3.5-27B ranked across the Mac lineup at the best practical quantization, using the best available runtime evidence.

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

Rank	Mac	Score	Quant	Tok/s	Runtime	Fits	Evidence	Price	Why it ranks here
1	Mac Studio M3 Ultra 256GB	446	8bit	38.0 tok/s	MLX	Fits	Estimated	$7,499	8bit is the current best practical quantization. 38.0 tok/s is estimated from nearby benchmark coverage. 228.4 GB headroom remains at this quantization.
2	Mac Pro M2 Ultra 192GB	313	8bit	20.6 tok/s	MLX	Fits	Estimated	$6,999	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 164.4 GB headroom remains at this quantization.
3	Mac Studio M4 Max 128GB	249	8bit	20.6 tok/s	MLX	Fits	Estimated	$4,499	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 100.4 GB headroom remains at this quantization.
4	MacBook Pro M4 Max 128GB 16-inch	249	8bit	20.6 tok/s	MLX	Fits	Estimated	$5,999	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 100.4 GB headroom remains at this quantization.
5	Mac Studio M3 Ultra 96GB	217	8bit	20.6 tok/s	MLX	Fits	Estimated	$3,999	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 68.4 GB headroom remains at this quantization.
6	Mac Studio M4 Max 64GB	185	8bit	20.6 tok/s	MLX	Fits	Estimated	$2,999	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 36.4 GB headroom remains at this quantization.
7	MacBook Pro M4 Max 64GB 16-inch	185	8bit	20.6 tok/s	MLX	Fits	Estimated	$4,499	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 36.4 GB headroom remains at this quantization.
8	Mac Studio M4 Max 48GB	169	8bit	20.6 tok/s	MLX	Fits	Estimated	$2,499	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 20.4 GB headroom remains at this quantization.
9	MacBook Pro M4 Max 48GB 14-inch	169	8bit	20.6 tok/s	MLX	Fits	Estimated	$3,499	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 20.4 GB headroom remains at this quantization.
10	MacBook Pro M4 Max 48GB 16-inch	169	8bit	20.6 tok/s	MLX	Fits	Estimated	$3,999	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 20.4 GB headroom remains at this quantization.
11	Mac Studio M4 Max 36GB	157	8bit	20.6 tok/s	MLX	Fits	Estimated	$1,999	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 8.4 GB headroom remains at this quantization.
12	MacBook Pro M4 Max 36GB 14-inch	157	8bit	20.6 tok/s	MLX	Fits	Estimated	$2,999	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 8.4 GB headroom remains at this quantization.
13	MacBook Pro M4 Max 36GB 16-inch	157	8bit	20.6 tok/s	MLX	Fits	Estimated	$3,499	8bit is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 8.4 GB headroom remains at this quantization.
14	Mac Mini M4 32GB	151	Q6_K	20.6 tok/s	MLX	Fits	Estimated	$799	Q6_K is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 8.9 GB headroom remains at this quantization.
15	MacBook Air M4 32GB 13-inch	151	Q6_K	20.6 tok/s	MLX	Fits	Estimated	$1,499	Q6_K is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 8.9 GB headroom remains at this quantization.
16	MacBook Air M4 32GB 15-inch	151	Q6_K	20.6 tok/s	MLX	Fits	Estimated	$1,699	Q6_K is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 8.9 GB headroom remains at this quantization.
17	Mac Mini M4 24GB	146	Q5_K_M	20.6 tok/s	MLX	Fits	Estimated	$599	Q5_K_M is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 3.6 GB headroom remains at this quantization.
18	MacBook Air M4 24GB 13-inch	146	Q5_K_M	20.6 tok/s	MLX	Fits	Estimated	$1,299	Q5_K_M is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 3.6 GB headroom remains at this quantization.
19	Mac Mini M4 Pro 24GB	146	Q5_K_M	20.6 tok/s	MLX	Fits	Estimated	$1,399	Q5_K_M is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 3.6 GB headroom remains at this quantization.
20	MacBook Air M4 24GB 15-inch	146	Q5_K_M	20.6 tok/s	MLX	Fits	Estimated	$1,499	Q5_K_M is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 3.6 GB headroom remains at this quantization.
21	MacBook Pro M4 Pro 24GB 14-inch	146	Q5_K_M	20.6 tok/s	MLX	Fits	Estimated	$1,999	Q5_K_M is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 3.6 GB headroom remains at this quantization.
22	MacBook Pro M4 Pro 24GB 16-inch	146	Q5_K_M	20.6 tok/s	MLX	Fits	Estimated	$2,499	Q5_K_M is the current best practical quantization. 20.6 tok/s is estimated from nearby benchmark coverage. 3.6 GB headroom remains at this quantization.
23	Mac Mini M4 Pro 48GB	128	8bit	8.5 tok/s	MLX	Fits	Measured	$1,599	8bit is the current best practical quantization. 8.5 tok/s is directly measured here. 20.4 GB headroom remains at this quantization.
24	MacBook Pro M4 Pro 48GB 14-inch	128	8bit	8.5 tok/s	MLX	Fits	Measured	$2,499	8bit is the current best practical quantization. 8.5 tok/s is directly measured here. 20.4 GB headroom remains at this quantization.
25	MacBook Pro M4 Pro 48GB 16-inch	128	8bit	8.5 tok/s	MLX	Fits	Measured	$2,999	8bit is the current best practical quantization. 8.5 tok/s is directly measured here. 20.4 GB headroom remains at this quantization.
26	Mac Mini M4 16GB	29	Q2_K	0.0 tok/s	llama.cpp	Fits	Estimated	$499	Q2_K is the current best practical quantization. 0.0 tok/s is estimated from nearby benchmark coverage. 5.2 GB headroom remains at this quantization.
27	MacBook Air M4 16GB 13-inch	29	Q2_K	0.0 tok/s	llama.cpp	Fits	Estimated	$1,099	Q2_K is the current best practical quantization. 0.0 tok/s is estimated from nearby benchmark coverage. 5.2 GB headroom remains at this quantization.
28	MacBook Air M4 16GB 15-inch	29	Q2_K	0.0 tok/s	llama.cpp	Fits	Estimated	$1,299	Q2_K is the current best practical quantization. 0.0 tok/s is estimated from nearby benchmark coverage. 5.2 GB headroom remains at this quantization.

Qwen3.5-27B — ranking first, raw rows below

Start with the ranked Mac table above. Use the rest of this page to inspect raw Apple Silicon coverage and model metadata.

Quantizations observed: 4bit, 8bit, Q4_K - Medium, Q6_K

11Benchmark rows

8Chip tiers covered

38.0Fastest avg tok/s (M3 Ultra (256 GB))

15.3 GBMinimum RAM observed

What this page answers best

Fastest published result is 38.0 tok/s on M3 Ultra (256 GB) at 4bit. Smallest published fit is 15.3 GB on M3 Ultra (256 GB). Longest published context on this page is 8k. Published runtimes include llama.cpp, MLX. Start with Rankings for the decision, then use the raw rows below to audit the evidence.

Evidence state: 11 linked reference rows and no Silicon Score Lab rows yet.

Published runtimes here: 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

Catalog record

27BTotal params

DenseActive params

262,144Context window

2026-02-24Release 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

Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.

Official source · Raw model card

agentscodingreasoningvisual-understanding

Runtime support mentioned

vLLMSGLangTransformersKTransformers

Official takeaways

Type: Causal Language Model with Vision Encoder.
Scale: 27B.
Context: 262,144 natively and extensible up to 1,010,000 tokens..
Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding ben…

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

Field reality on Apple Silicon

Qwen3.5-27B: 8 Apple Silicon field reports; best reported generation ~31.6 tok/s; seen on MacBook Pro M5 MAX 128GB, M2 ULTRA 128GB, MacBook Pro M4 PRO; via MLX, llama.cpp.

11Benchmark rows

8Field reports

8Practitioner signals

Sparse BenchmarksEvidence status

What practitioners keep saying

Reported as a meaningful step up from older 27B-32B daily-driver choices for coding.
Apple Silicon viability discussion is centered on the M4 Pro 48GB class, which is a practical buyer tier.
The discussion is about what people can actually live with on a Mac Mini class machine, not lab-only fit math.

Runtime mentions in the field

Claude Codellama.cppLM StudioMLXOllamaOpenClaw

Hardware mentioned in reports

16GB24GB32GB48GB64GB128GBM1 MaxM4

What would improve confidence

Capture Practitioner Runtime Notes
Queue Lab Verification If Hardware Available
Reproduce Field Performance Signal
Upgrade To First Party Measurement

Current published coverage

Published chip coverage includes M3 Ultra (256 GB), M5 Max (128 GB), M5 Max (48 GB), M2 Ultra (GPU count not published, 128 GB), M1 Max (64 GB) plus 3 more chip tiers. Fastest published row is 38.0 tok/s on M3 Ultra (256 GB) at 4bit. Lowest published RAM requirement is 15.3 GB on M3 Ultra (256 GB). Catalog context window is 8k.

M3 Ultra (256 GB)M5 Max (128 GB)M5 Max (48 GB)M2 Ultra (GPU count not published, 128 GB)M1 Max (64 GB)M5 Pro (64 GB)M4 Pro (48 GB)M4 (16 GB)

Related Qwen3.5-27B models with published pages: Qwen3.5-35B-A3B · Qwen3.5-9B · Qwen3.5-122B-A10B · Qwen3.5-397B-A17B

Standardized eval scorecards for Qwen3.5-27B

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 76%

83%Tools

90%Coding

50%Reasoning

80%General

Speed and memory

Long decode: 37.7 tok/s
Short decode: 17.7 tok/s
Cold TTFT: 0.453 s
Active RAM: 15.3 GB

A strong fits-anywhere coding and tool-use compromise.

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

Raw benchmark rows for Qwen3.5-27B

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	15.3 GB	—	38.0 tok/s	—	MLX	ref
M5 Max (128 GB)	4bit	—	—	31.6 tok/s	—	MLX	ref
M5 Max (48 GB)	4bit	—	8k	31.3 tok/s	779.0 tok/s	MLX	ref
M2 Ultra (GPU count not published, 128 GB)	8bit	—	—	27.1 tok/s	—	MLX	ref
M5 Max (48 GB)	Q4_K - Medium	—	8k	23.7 tok/s	171.0 tok/s	llama.cpp	ref
M2 Ultra (GPU count not published, 128 GB)	8bit	—	—	20.6 tok/s	—	MLX	ref
M5 Max (128 GB)	Q6_K	—	8k	16.5 tok/s	—	llama.cpp	ref
M1 Max (64 GB)	4bit	—	8k	15.0 tok/s	67.0 tok/s	MLX	ref
M5 Pro (64 GB)	Q4_K - Medium	24.9 GB	—	10.0 tok/s	—	llama.cpp	ref
M4 Pro (48 GB)	8bit	—	—	8.5 tok/s	—	MLX	ref
M4 (16 GB)	Q4_K - Medium	—	—	0.0 tok/s	—	llama.cpp	ref

Chips with published results for Qwen3.5-27B

M3 Ultra (256 GB)M5 Max (128 GB)M5 Max (48 GB)M2 Ultra (GPU count not published, 128 GB)M1 Max (64 GB)M5 Pro (64 GB)M4 Pro (48 GB)M4 (16 GB)

Data

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

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