BGE Large EN v1.5 needs ~6.8 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With F16 quantization, expect ~5 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
4.7 tok/s
TTFT
41279 ms
Safe context
512
Memory
6.8 GB / 23.0 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 4.7 tok/s | 22516 ms | 512 |
| Coding | A | Runs well | 4.7 tok/s | 41279 ms | 512 |
| Agentic Coding | A | Runs well | 4.7 tok/s | 60043 ms | 512 |
| Reasoning | A | Runs well | 4.7 tok/s | 48785 ms | 512 |
| RAG | A | Runs well | 4.7 tok/s | 75053 ms | 512 |
How BGE Large EN v1.5 (0.33500000834465027B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | A75 |
Q3_K_S | 3 | 0.2 GB | Low | A75 |
NVFP4 | 4 |
Copy-paste commands to run BGE Large EN v1.5 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "BAAI/bge-large-en-v1.5" \
--hf-file "bge-large-en-v1.5-F16.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 29.4 tok/s | |
| Qwen 3.5 27B | 27B | A | 13.1 tok/s |
Yes, MacBook Pro M1 Max 32GB can run BGE Large EN v1.5 with a A grade (Runs well). Expected decode speed: 4.7 tok/s.
BGE Large EN v1.5 (0.33500000834465027B parameters) requires approximately 6.8 GB of memory with F16 quantization.
The recommended quantization for BGE Large EN v1.5 is F16, which balances quality and memory efficiency.
On MacBook Pro M1 Max 32GB, BGE Large EN v1.5 achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41279ms using F16 quantization.
For coding workloads, BGE Large EN v1.5 on MacBook Pro M1 Max 32GB receives a A grade with 4.7 tok/s and 512 context.
On MacBook Pro M1 Max 32GB, BGE Large EN v1.5 can safely use up to 512 tokens of context. The model's official context limit is 512, 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-large-en-v1.5-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>
Preview:
0.2 GB |
| Medium |
| A75 |
Q4_K_M | 4 | 0.2 GB | Medium | A75 |
Q5_K_M | 5 | 0.2 GB | High | A75 |
Q6_K | 6 | 0.3 GB | High | A75 |
Q8_0 | 8 | 0.4 GB | Very High | A75 |
F16Best for your GPU | 16 | 0.7 GB | Maximum | A75 |
| Qwen 3.6 27B | 27B | S | 14.5 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | A | 30.9 tok/s |
| Qwen 3.5 9B | 9B | S | 43.1 tok/s |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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.