MacBook Pro M4 Pro 24GBBudget pick
24 GB Unified (+8)
C
This setup is broadly balanced for this model.52.8 tok/s decode
~$1,999 MSRP
stablelm 2 1 6b chat imatrix needs ~7.2 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~57 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
57.4 tok/s
TTFT
3370 ms
Safe context
217K
Memory
7.2 GB / 16.0 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 | 57.4 tok/s | 1838 ms | 217K |
| Coding | C | Runs well | 57.4 tok/s | 3370 ms | 217K |
| Agentic Coding | C | Runs well | 57.4 tok/s | 4902 ms | 217K |
| Reasoning | C | Runs well | 57.4 tok/s | 3983 ms | 217K |
| RAG | C | Runs well | 57.4 tok/s | 6128 ms | 217K |
Inference speed
Estimated decode speed (tokens/sec) for stablelm 2 1 6b chat imatrix 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? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 114.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 84.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 84.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 84.0 | Fits |
| RTX 4070 12GB | 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 |
| RTX 3060 12GB | 12 GB | Q4_K_M | 64.9 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 60.1 | Fits |
| RTX 4060 8GB | 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 1 6b chat imatrix (6B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C46 |
Q3_K_S | 3 | 2.9 GB | Low | C47 |
NVFP4 | 4 | 3.4 GB | Medium | C47 |
Q4_K_M | 4 | 3.7 GB | Medium | C47 |
Q5_K_M | 5 | 4.3 GB | High | C48 |
Q6_K | 6 | 4.9 GB | High | C48 |
Q8_0 | 8 | 6.4 GB | Very High | C50 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Copy-paste commands to run stablelm 2 1 6b chat imatrix on your machine.
Run
lms load hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf && lms server startUpgrade options
Yes, RTX 4060 Ti 16GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 57.4 tok/s.
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 7.2 GB of memory with Q4_K_M quantization.
The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.
On RTX 4060 Ti 16GB, stablelm 2 1 6b chat imatrix achieves approximately 57.4 tokens per second decode speed with a time-to-first-token of 3370ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on RTX 4060 Ti 16GB receives a C grade with 57.4 tok/s and 217K context.
On RTX 4060 Ti 16GB, stablelm 2 1 6b chat imatrix can safely use up to 217K 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-crataco--stablelm-2-1-6b-chat-imatrix-gguf-on-rtx-4060-ti-16gb" 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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