MacBook Pro M3 Pro 18GB经济之选
18 GB Unified (+6)
C
This setup is broadly balanced for this model.42 tok/s 解码
~$1,999 MSRP
Can stablelm 3b 4e1t run on RTX 4000 Ada Laptop 12GB?
YES — Runs Great
C47Usable
Estimated from fit model
stablelm 3b 4e1t needs ~4.6 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 4.6 GB, 42.0 tok/s, Runs well
4.6 GB required12.0 GB available
38% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
354K
Memory
4.6 GB / 12.0 GB
Memory breakdown
Weights1.8 GB
KV Cache0.4 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsstablelm 3b 4e1t on RTX 4000 Ada Laptop 12GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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 | 42.0 tok/s | 2514 ms | 354K |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 354K |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 354K |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 354K |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 354K |
Inference speed
stablelm 3b 4e1t inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for stablelm 3b 4e1t at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~57 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 | 57.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 48.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 48.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 42.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 42.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 42.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 42.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 42.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 42.0 | 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.
Quantization options
How stablelm 3b 4e1t (3B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C47 |
Q3_K_S | 3 | 1.5 GB | Low | C47 |
NVFP4 | 4 | 1.7 GB | Medium | C47 |
Q4_K_M | 4 | 1.8 GB | Medium | C48 |
Q5_K_M | 5 | 2.2 GB | High | C48 |
Q6_K | 6 | 2.5 GB | High | C48 |
Q8_0 | 8 | 3.2 GB | Very High | C49 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | C52 |
Get started
Copy-paste commands to run stablelm 3b 4e1t on your machine.
Run
lms load hf-afrideva--stablelm-3b-4e1t-gguf && lms server start升级选项
能流畅运行 stablelm 3b 4e1t 的硬件
Frequently asked questions
Can RTX 4000 Ada Laptop 12GB run stablelm 3b 4e1t?
Yes, RTX 4000 Ada Laptop 12GB can run stablelm 3b 4e1t with a C grade (Runs well). Expected decode speed: 42.0 tok/s.
How much VRAM does stablelm 3b 4e1t need?
stablelm 3b 4e1t (3B parameters) requires approximately 4.6 GB of memory with Q4_K_M quantization.
What is the best quantization for stablelm 3b 4e1t?
The recommended quantization for stablelm 3b 4e1t is Q4_K_M, which balances quality and memory efficiency.
What speed will stablelm 3b 4e1t run at on RTX 4000 Ada Laptop 12GB?
On RTX 4000 Ada Laptop 12GB, stablelm 3b 4e1t achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
Can RTX 4000 Ada Laptop 12GB run stablelm 3b 4e1t for coding?
For coding workloads, stablelm 3b 4e1t on RTX 4000 Ada Laptop 12GB receives a C grade with 42.0 tok/s and 354K context.
What context window can stablelm 3b 4e1t use on RTX 4000 Ada Laptop 12GB?
On RTX 4000 Ada Laptop 12GB, stablelm 3b 4e1t can safely use up to 354K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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