stabilityai japanese stablelm base gamma 7b needs ~25.5 GB VRAM. B100 192GB has 192.0 GB. With Q4_K_M quantization, expect ~98 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
98.0 tok/s
TTFT
1976 ms
Safe context
3.3M
Memory
25.5 GB / 192.0 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 | 98.0 tok/s | 1078 ms | 3.3M |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 3.3M |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 3.3M |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 3.3M |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 3.3M |
Inference speed
Estimated decode speed (tokens/sec) for stabilityai japanese stablelm base gamma 7b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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 | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 88.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 55.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.5 | Tight |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
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 base gamma 7b (7B params) fits at each quantization level on B100 192GB (192.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | D37 |
Q3_K_S | 3 | 3.4 GB | Low | D37 |
NVFP4 | 4 | 3.9 GB | Medium | D37 |
Q4_K_M | 4 | 4.3 GB | Medium | D37 |
Q5_K_M | 5 | 5.0 GB | High | D37 |
Q6_K | 6 | 5.7 GB | High | D37 |
Q8_0 | 8 | 7.5 GB | Very High | D37 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | D37 |
Copy-paste commands to run stabilityai japanese stablelm base gamma 7b on your machine.
Run
lms load hf-richarderkhov--stabilityai---japanese-stablelm-base-gamma-7b-gguf && lms server startYes, B100 192GB can run stabilityai japanese stablelm base gamma 7b with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
stabilityai japanese stablelm base gamma 7b (7B parameters) requires approximately 25.5 GB of memory with Q4_K_M quantization.
The recommended quantization for stabilityai japanese stablelm base gamma 7b is Q4_K_M, which balances quality and memory efficiency.
On B100 192GB, stabilityai japanese stablelm base gamma 7b achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, stabilityai japanese stablelm base gamma 7b on B100 192GB receives a C grade with 98.0 tok/s and 3.3M context.
On B100 192GB, stabilityai japanese stablelm base gamma 7b can safely use up to 3.3M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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