BGE M3 needs ~31.1 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With F16 quantization, expect ~8 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
8.0 tok/s
TTFT
24346 ms
Safe context
8K
Memory
31.1 GB / 184.3 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 | 8.0 tok/s | 13280 ms | 8K |
| Coding | A | Runs well | 8.0 tok/s | 24346 ms | 8K |
| Agentic Coding | A | Runs well | 8.0 tok/s | 35412 ms | 8K |
| Reasoning | A | Runs well | 8.0 tok/s | 28773 ms | 8K |
| RAG | A | Runs well | 8.0 tok/s | 44266 ms | 8K |
How BGE M3 (0.5680000185966492B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | A73 |
Q3_K_S | 3 | 0.3 GB | Low | A73 |
NVFP4 | 4 |
Copy-paste commands to run BGE M3 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "BAAI/bge-m3" \
--hf-file "bge-m3-F16.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 8.1 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S |
Yes, Mac Studio M3 Ultra 256GB can run BGE M3 with a A grade (Runs well). Expected decode speed: 8.0 tok/s.
BGE M3 (0.5680000185966492B parameters) requires approximately 31.1 GB of memory with F16 quantization.
The recommended quantization for BGE M3 is F16, which balances quality and memory efficiency.
On Mac Studio M3 Ultra 256GB, BGE M3 achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24346ms using F16 quantization.
For coding workloads, BGE M3 on Mac Studio M3 Ultra 256GB receives a A grade with 8.0 tok/s and 8K context.
On Mac Studio M3 Ultra 256GB, BGE M3 can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/bge-m3-on-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.3 GB |
| Medium |
| A73 |
Q4_K_M | 4 | 0.3 GB | Medium | A73 |
Q5_K_M | 5 | 0.4 GB | High | A73 |
Q6_K | 6 | 0.5 GB | High | A73 |
Q8_0 | 8 | 0.6 GB | Very High | A73 |
F16Best for your GPU | 16 | 1.2 GB | Maximum | A73 |
| 84.2 tok/s |
| Qwen 3.5 27B | 27B | S | 36.5 tok/s |
| Qwen 3.6 27B | 27B | S | 36.6 tok/s |
| Qwen 3.5 122B A10B | 122B | S | 34.7 tok/s |
Not always. Mac Studio M3 Ultra 256GB 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.