stablelm 2 zephyr 1 6b needs ~6.1 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~46 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
46.4 tok/s
TTFT
4170 ms
Safe context
60K
Memory
6.1 GB / 8.0 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 | 46.4 tok/s | 2275 ms | 60K |
| Coding | C | Runs well | 46.4 tok/s | 4170 ms | 60K |
| Agentic Coding | C | Tight fit | 46.4 tok/s | 6066 ms | 60K |
| Reasoning | C | Runs well | 46.4 tok/s | 4928 ms | 60K |
| RAG | C | Tight fit | 46.4 tok/s | 7582 ms | 60K |
Inference speed
Estimated decode speed (tokens/sec) for stablelm 2 zephyr 1 6b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~114 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? |
|---|---|---|---|---|
| 32 GB | Q4_K_M | 114.0 | Fits | |
| 24 GB | Q4_K_M | 84.0 | Fits | |
| 16 GB | Q4_K_M | 84.0 | Fits | |
| 24 GB | Q4_K_M | 84.0 | Fits | |
| 12 GB | Q4_K_M | 84.0 | Fits | |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 84.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 84.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 84.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 84.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 84.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 84.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 65.6 | Fits |
| 12 GB | Q4_K_M | 64.9 | Fits | |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 60.1 | Fits |
| 8 GB | Q4_K_M | 54.3 | Fits | |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 52.8 | 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 stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C52 |
Q3_K_S | 3 | 2.9 GB | Low | C54 |
NVFP4 | 4 | 3.4 GB | Medium | C54 |
Q4_K_M | 4 | 3.7 GB | Medium | C53 |
Q5_K_M | 5 | 4.3 GB | High | C53 |
Q6_KBest for your GPU | 6 | 4.9 GB | High | C53 |
Q8_0 | 8 | 6.4 GB | Very High | F0 |
F16 | 16 | 12.3 GB | Maximum | F0 |
Copy-paste commands to run stablelm 2 zephyr 1 6b on your machine.
Run
lms load hf-stabilityai--stablelm-2-zephyr-1-6b && lms server startYes, Radeon PRO W7600 8GB can run stablelm 2 zephyr 1 6b with a C grade (Runs well). Expected decode speed: 46.4 tok/s.
stablelm 2 zephyr 1 6b (6B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
The recommended quantization for stablelm 2 zephyr 1 6b is Q4_K_M, which balances quality and memory efficiency.
On Radeon PRO W7600 8GB, stablelm 2 zephyr 1 6b achieves approximately 46.4 tokens per second decode speed with a time-to-first-token of 4170ms using Q4_K_M quantization.
For coding workloads, stablelm 2 zephyr 1 6b on Radeon PRO W7600 8GB receives a C grade with 46.4 tok/s and 60K context.
On Radeon PRO W7600 8GB, stablelm 2 zephyr 1 6b can safely use up to 60K 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-stabilityai--stablelm-2-zephyr-1-6b-on-radeon-pro-w7600-8gb" 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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