RTX PRO 5000 Blackwell 48GBBudget pick1344 GB/s (+544)
C
Raises estimated decode speed by about 111%.100 tok/s decode
Raises estimated decode speed by about 111%.
~$4,999 MSRP
StableLM 2 12B needs ~28.7 GB VRAM. Mac Studio M1 Ultra 64GB has 46.1 GB. With Q5_K_M quantization, expect ~48 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
47.5 tok/s
TTFT
4075 ms
Safe context
4K
Memory
28.7 GB / 46.1 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 | C | Runs well | 47.5 tok/s | 2223 ms | 4K |
| Coding | C | Runs well | 47.5 tok/s | 4075 ms | 4K |
| Agentic Coding | C | Tight fit | 47.5 tok/s | 5927 ms | 4K |
| Reasoning | C | Runs well | 47.5 tok/s | 4816 ms | 4K |
| RAG | C | Tight fit | 47.5 tok/s | 7409 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for StableLM 2 12B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~103 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 | Q5_K_M | 103.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 60.1 | Fits |
| RTX 4090 24GB | 24 GB | Q5_K_M | 53.5 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 50.1 | Fits |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 47.8 | Offloads |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 47.5 | Fits |
| RTX 3090 24GB | 24 GB | Q5_K_M | 45.3 | Offloads |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 32.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 32.7 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 25.9 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 23.8 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 23.4 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 20.0 | Fits |
| RTX 4070 12GB | 12 GB | Q5_K_M | 8.2 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 4.8 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 3.4 | Too big |
Estimates for single-stream decoding at Q5_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 StableLM 2 12B (12B params) fits at each quantization level on Mac Studio M1 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C42 |
Q3_K_S | 3 | 5.9 GB | Low | C42 |
NVFP4 | 4 | 6.7 GB | Medium | C42 |
Q4_K_M | 4 | 7.3 GB | Medium | C42 |
Q5_K_M | 5 | 8.6 GB | High | C42 |
Q6_K | 6 | 9.8 GB | High | C43 |
Q8_0 | 8 | 12.8 GB | Very High | C44 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | C48 |
Copy-paste commands to run StableLM 2 12B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "stabilityai/stablelm-2-12b-chat" \
--hf-file "stablelm-2-12b-chat-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Yes, Mac Studio M1 Ultra 64GB can run StableLM 2 12B with a C grade (Runs well). Expected decode speed: 47.5 tok/s.
StableLM 2 12B (12B parameters) requires approximately 28.7 GB of memory with Q5_K_M quantization.
The recommended quantization for StableLM 2 12B is Q5_K_M, which balances quality and memory efficiency.
On Mac Studio M1 Ultra 64GB, StableLM 2 12B achieves approximately 47.5 tokens per second decode speed with a time-to-first-token of 4075ms using Q5_K_M quantization.
For coding workloads, StableLM 2 12B on Mac Studio M1 Ultra 64GB receives a C grade with 47.5 tok/s and 4K context.
On Mac Studio M1 Ultra 64GB, StableLM 2 12B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Not always. Mac Studio M1 Ultra 64GB 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/stablelm-2-12b-on-m1-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: