OLMo 2 13B needs ~13.9 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~9 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
9.3 tok/s
TTFT
20905 ms
Safe context
33K
Memory
13.9 GB / 17.3 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 | 9.3 tok/s | 11403 ms | 33K |
| Coding | A | Runs well | 9.3 tok/s | 20905 ms | 33K |
| Agentic Coding | A | Tight fit | 9.3 tok/s | 30407 ms | 33K |
| Reasoning | A | Runs well | 9.3 tok/s | 24706 ms | 33K |
| RAG | A | Tight fit | 9.3 tok/s | 38009 ms | 33K |
Inference speed
Estimated decode speed (tokens/sec) for OLMo 2 13B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~159 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 | 158.6 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 109.6 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 94.1 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 87.4 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 87.4 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 75.8 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 63.2 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 59.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 41.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 41.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 37.4 | Offloads |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 32.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 30.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 25.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 21.9 | Offloads |
| RTX 4060 8GB | 8 GB | Q4_K_M | 9.0 | Too big |
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 OLMo 2 13B (13B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | A75 |
Q3_K_S | 3 | 6.4 GB | Low | A76 |
NVFP4 | 4 | 7.3 GB | Medium | A77 |
Q4_K_M | 4 | 7.9 GB | Medium | A78 |
Q5_K_M | 5 | 9.4 GB | High | A78 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | A78 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Copy-paste commands to run OLMo 2 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "allenai/OLMo-2-13B-Instruct" \
--hf-file "OLMo-2-13B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Magistral Small 2507 | 24B | B | 3.8 tok/s | |
| Devstral Small 2 24B Instruct | 24B | B | 3.8 tok/s | |
 Qwen 3 14B | 14B | S | 8.6 tok/s | |
 Phi-4-reasoning-plus 14B | 14.7B | S | 8.2 tok/s | |
| Devstral Small 1.1 | 24B | B | 3.8 tok/s |
Yes, MacBook Pro M3 24GB can run OLMo 2 13B with a A grade (Runs well). Expected decode speed: 9.3 tok/s.
OLMo 2 13B (13B parameters) requires approximately 13.9 GB of memory with Q4_K_M quantization.
The recommended quantization for OLMo 2 13B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 24GB, OLMo 2 13B achieves approximately 9.3 tokens per second decode speed with a time-to-first-token of 20905ms using Q4_K_M quantization.
For coding workloads, OLMo 2 13B on MacBook Pro M3 24GB receives a A grade with 9.3 tok/s and 33K context.
On MacBook Pro M3 24GB, OLMo 2 13B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 24GB 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/olmo-2-13b-on-m3-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: