MacBook Pro M4 Max 48GBコスパ重視
48 GB Unified (+16)
C
This setup is broadly balanced for this model.84 tok/sデコード
〜$2,499 MSRP
Can stablelm 2 1 6b chat imatrix run on RTX 5090 32GB?
YES — Runs Great
C47Usable
Estimated from fit model
stablelm 2 1 6b chat imatrix needs ~8.5 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~114 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 8.5 GB, 114.0 tok/s, Runs well
8.5 GB required32.0 GB available
27% VRAM used
Fit status
Runs well
Decode
114.0 tok/s
TTFT
1698 ms
Safe context
552K
Memory
8.5 GB / 32.0 GB
Memory breakdown
Weights3.7 GB
KV Cache0.7 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsstablelm 2 1 6b chat imatrix on RTX 5090 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 114.0 tok/s decode · 1.7s TTFT (warm) · 285 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 114.0 tok/s | 926 ms | 552K |
| Coding | C | Runs well | 114.0 tok/s | 1698 ms | 552K |
| Agentic Coding | C | Runs well | 114.0 tok/s | 2470 ms | 552K |
| Reasoning | C | Runs well | 114.0 tok/s | 2007 ms | 552K |
| RAG | C | Runs well | 114.0 tok/s | 3088 ms | 552K |
Quantization options
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C43 |
Q3_K_S | 3 | 2.9 GB | Low | C43 |
NVFP4 | 4 | 3.4 GB | Medium | C43 |
Q4_K_M | 4 | 3.7 GB | Medium | C43 |
Q5_K_M | 5 | 4.3 GB | High | C43 |
Q6_K | 6 | 4.9 GB | High | C43 |
Q8_0 | 8 | 6.4 GB | Very High | C44 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C47 |
Get started
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 startアップグレードオプション
stablelm 2 1 6b chat imatrixを快適に動かすハードウェア
Frequently asked questions
Can RTX 5090 32GB run stablelm 2 1 6b chat imatrix?
Yes, RTX 5090 32GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 114.0 tok/s.
How much VRAM does stablelm 2 1 6b chat imatrix need?
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.
What is the best quantization for stablelm 2 1 6b chat imatrix?
The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.
What speed will stablelm 2 1 6b chat imatrix run at on RTX 5090 32GB?
On RTX 5090 32GB, stablelm 2 1 6b chat imatrix achieves approximately 114.0 tokens per second decode speed with a time-to-first-token of 1698ms using Q4_K_M quantization.
Can RTX 5090 32GB run stablelm 2 1 6b chat imatrix for coding?
For coding workloads, stablelm 2 1 6b chat imatrix on RTX 5090 32GB receives a C grade with 114.0 tok/s and 552K context.
What context window can stablelm 2 1 6b chat imatrix use on RTX 5090 32GB?
On RTX 5090 32GB, stablelm 2 1 6b chat imatrix can safely use up to 552K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf-on-rtx-5090-32gb" 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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