stabilityai japanese stablelm instruct beta 70b needs ~70.1 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~157 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
157.4 tok/s
TTFT
1230 ms
Safe context
230K
Memory
70.1 GB / 180.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 157.4 tok/s | 671 ms | 230K |
| Coding | C | Runs well | 157.4 tok/s | 1230 ms | 230K |
| Agentic Coding | C | Runs well | 157.4 tok/s | 1789 ms | 230K |
| Reasoning | C | Runs well | 157.4 tok/s | 1454 ms | 230K |
| RAG | C | Runs well | 157.4 tok/s | 2237 ms | 230K |
Inference speed
Estimated decode speed (tokens/sec) for stabilityai japanese stablelm instruct beta 70b at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 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? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 14.6 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 14.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 13.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 10.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 10.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 9.9 | Too big |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 7.7 | Heavy offload |
| RTX 5090 32GB | 32 GB | Q4_K_M | 7.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 7.0 | Heavy offload |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 6.2 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 4.7 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 4.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 3.6 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.7 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.4 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.3 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.1 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | 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 stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | D38 |
Q3_K_S | 3 | 34.3 GB | Low | D39 |
NVFP4 | 4 | 39.2 GB | Medium | D40 |
Q4_K_M | 4 | 42.7 GB | Medium | C40 |
Q5_K_M | 5 | 50.4 GB | High | C41 |
Q6_K | 6 | 57.4 GB | High | C42 |
Q8_0 | 8 | 74.9 GB | Very High | C44 |
F16Best for your GPU | 16 | 143.5 GB | Maximum | C47 |
Copy-paste commands to run stabilityai japanese stablelm instruct beta 70b on your machine.
Run
lms load hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf && lms server startYes, NVIDIA B200 180GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 157.4 tok/s.
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 70.1 GB of memory with Q4_K_M quantization.
The recommended quantization for stabilityai japanese stablelm instruct beta 70b is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA B200 180GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 157.4 tokens per second decode speed with a time-to-first-token of 1230ms using Q4_K_M quantization.
For coding workloads, stabilityai japanese stablelm instruct beta 70b on NVIDIA B200 180GB receives a C grade with 157.4 tok/s and 230K context.
On NVIDIA B200 180GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 230K tokens of context. The model's official context limit is —, 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/hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf-on-b200-180gb" 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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