MacBook Pro M4 Max 36GBOpción económica
36 GB Unified (+4)410 GB/s (+210)
C
Sube la velocidad estimada de decodificación alrededor de un 117%.76.9 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 117%.
~$2,499 MSRP
Can stablelm 2 1 6b chat imatrix run on MacBook Pro M1 Pro 32GB?
YES — Runs Great
C47Usable
Estimated from fit model
stablelm 2 1 6b chat imatrix needs ~8.7 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~36 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 8.7 GB, 35.5 tok/s, Runs well
8.7 GB required23.0 GB available
38% VRAM used
Fit status
Runs well
Decode
35.5 tok/s
TTFT
5451 ms
Safe context
342K
Memory
8.7 GB / 23.0 GB
Memory breakdown
Weights3.7 GB
KV Cache0.7 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsstablelm 2 1 6b chat imatrix on MacBook Pro M1 Pro 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: 35.5 tok/s decode · 5.5s TTFT (warm) · 89 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 35.5 tok/s | 2973 ms | 342K |
| Coding | C | Runs well | 35.5 tok/s | 5451 ms | 342K |
| Agentic Coding | C | Runs well | 35.5 tok/s | 7928 ms | 342K |
| Reasoning | C | Runs well | 35.5 tok/s | 6442 ms | 342K |
| RAG | C | Runs well | 35.5 tok/s | 9910 ms | 342K |
Quantization options
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C44 |
Q3_K_S | 3 | 2.9 GB | Low | C44 |
NVFP4 | 4 | 3.4 GB | Medium | C45 |
Q4_K_M | 4 | 3.7 GB | Medium | C45 |
Q5_K_M | 5 | 4.3 GB | High | C45 |
Q6_K | 6 | 4.9 GB | High | C45 |
Q8_0 | 8 | 6.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
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 startOpciones de mejora
Hardware que ejecuta bien stablelm 2 1 6b chat imatrix
MacBook Pro M4 Max 48GBMejor relación calidad-precio
48 GB Unified (+16)546 GB/s (+346)
C
Sube la velocidad estimada de decodificación alrededor de un 137%.84 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 137%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
MacBook Pro M3 Max 48GBMejora Apple
48 GB Unified (+16)400 GB/s (+200)
C
Sube la velocidad estimada de decodificación alrededor de un 85%.65.6 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 85%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Frequently asked questions
Can MacBook Pro M1 Pro 32GB run stablelm 2 1 6b chat imatrix?
Yes, MacBook Pro M1 Pro 32GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 35.5 tok/s.
How much VRAM does stablelm 2 1 6b chat imatrix need?
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 8.7 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 MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, stablelm 2 1 6b chat imatrix achieves approximately 35.5 tokens per second decode speed with a time-to-first-token of 5451ms using Q4_K_M quantization.
Can MacBook Pro M1 Pro 32GB run stablelm 2 1 6b chat imatrix for coding?
For coding workloads, stablelm 2 1 6b chat imatrix on MacBook Pro M1 Pro 32GB receives a C grade with 35.5 tok/s and 342K context.
What context window can stablelm 2 1 6b chat imatrix use on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, stablelm 2 1 6b chat imatrix can safely use up to 342K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for stablelm 2 1 6b chat imatrix?
Not always. MacBook Pro M1 Pro 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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