LFM2.5 8B A1B needs ~9.7 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~103 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
103.4 tok/s
TTFT
1873 ms
Safe context
128K
Memory
9.7 GB / 23.0 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 | A | Runs well | 103.4 tok/s | 1021 ms | 128K |
| Coding | A | Runs well | 103.4 tok/s | 1873 ms | 128K |
| Agentic Coding | A | Runs well | 103.4 tok/s | 2724 ms | 128K |
| Reasoning | A | Runs well | 103.4 tok/s | 2213 ms | 128K |
| RAG | A | Runs well | 103.4 tok/s | 3405 ms | 128K |
Inference speed
Estimated decode speed (tokens/sec) for LFM2.5 8B A1B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~508 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 507.8 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 324.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 324.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 277.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 261.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 258.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 218.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 206.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 161.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 161.7 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 159.9 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 112.8 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 103.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 100.5 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 98.8 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 84.0 | Tight |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How LFM2.5 8B A1B (8.5B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.3 GB | Low | B69 |
Q3_K_S | 3 | 4.2 GB | Low | B70 |
NVFP4 | 4 | 4.8 GB | Medium | B70 |
Q4_K_M | 4 | 5.2 GB | Medium | A70 |
Q5_K_M | 5 | 6.1 GB | High | A71 |
Q6_K | 6 | 7.0 GB | High | A71 |
Q8_0 | 8 | 9.1 GB | Very High | A73 |
F16Best for your GPU | 16 | 17.4 GB | Maximum | A74 |
Copy-paste commands to run LFM2.5 8B A1B on your machine.
Run
lms load LFM2.5-8B-A1B && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 29.9 tok/s | |
| Qwen 3.5 27B | 27B | S | 13.3 tok/s | |
| Qwen 3.6 27B | 27B | S | 11 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 31.5 tok/s | |
| Qwen 3.5 9B | 9B | S | 43.1 tok/s |
Yes, MacBook Pro M1 Max 32GB can run LFM2.5 8B A1B with a A grade (Runs well). Expected decode speed: 103.4 tok/s.
LFM2.5 8B A1B (8.5B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.
The recommended quantization for LFM2.5 8B A1B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Max 32GB, LFM2.5 8B A1B achieves approximately 103.4 tokens per second decode speed with a time-to-first-token of 1873ms using Q4_K_M quantization.
For coding workloads, LFM2.5 8B A1B on MacBook Pro M1 Max 32GB receives a A grade with 103.4 tok/s and 128K context.
On MacBook Pro M1 Max 32GB, LFM2.5 8B A1B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. MacBook Pro M1 Max 32GB 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/lfm2.5-8b-a1b-on-m1-max-32gb" 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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