RTX 5090 32GBOpção econômica32 GB VRAM (+8)1792 GB/s (+784)
C
Adds memory headroom for longer context windows and future model growth.30.4 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can stablelm 2 zephyr 1.6b run on RTX 4090 24GB?
YES — Runs Great
C41Usable
Estimated from fit model
stablelm 2 zephyr 1.6b needs ~4.5 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~26 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) — 4.5 GB, 25.6 tok/s, Runs well
4.5 GB required24.0 GB available
19% VRAM used
Fit status
Runs well
Decode
25.6 tok/s
TTFT
7562 ms
Safe context
1.7M
Memory
4.5 GB / 24.0 GB
Memory breakdown
Weights1.0 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsstablelm 2 zephyr 1.6b on RTX 4090 24GB
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: 25.6 tok/s decode · 7.6s TTFT (warm) · 64 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 | 25.6 tok/s | 4125 ms | 1.6M |
| Coding | C | Runs well | 25.6 tok/s | 7562 ms | 1.7M |
| Agentic Coding | C | Runs well | 25.6 tok/s | 11000 ms | 1.7M |
| Reasoning | C | Runs well | 25.6 tok/s | 8937 ms | 1.7M |
| RAG | C | Runs well | 25.6 tok/s | 13750 ms | 1.7M |
Quantization options
How stablelm 2 zephyr 1.6b (1.600000023841858B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C43 |
Q3_K_S | 3 | 0.8 GB | Low | C43 |
NVFP4 | 4 | 0.9 GB | Medium | C43 |
Q4_K_M | 4 | 1.0 GB | Medium | C43 |
Q5_K_M | 5 | 1.2 GB | High | C43 |
Q6_K | 6 | 1.3 GB | High | C43 |
Q8_0 | 8 | 1.7 GB | Very High | C44 |
F16Best for your GPU | 16 | 3.3 GB | Maximum | C44 |
Get started
Copy-paste commands to run stablelm 2 zephyr 1.6b on your machine.
Run
lms load hf-second-state--stablelm-2-zephyr-1-6b-gguf && lms server startOpções de upgrade
Hardware que roda bem stablelm 2 zephyr 1.6b
Frequently asked questions
Can RTX 4090 24GB run stablelm 2 zephyr 1.6b?
Yes, RTX 4090 24GB can run stablelm 2 zephyr 1.6b with a C grade (Runs well). Expected decode speed: 25.6 tok/s.
How much VRAM does stablelm 2 zephyr 1.6b need?
stablelm 2 zephyr 1.6b (1.600000023841858B parameters) requires approximately 4.5 GB of memory with Q4_K_M quantization.
What is the best quantization for stablelm 2 zephyr 1.6b?
The recommended quantization for stablelm 2 zephyr 1.6b is Q4_K_M, which balances quality and memory efficiency.
What speed will stablelm 2 zephyr 1.6b run at on RTX 4090 24GB?
On RTX 4090 24GB, stablelm 2 zephyr 1.6b achieves approximately 25.6 tokens per second decode speed with a time-to-first-token of 7562ms using Q4_K_M quantization.
Can RTX 4090 24GB run stablelm 2 zephyr 1.6b for coding?
For coding workloads, stablelm 2 zephyr 1.6b on RTX 4090 24GB receives a C grade with 25.6 tok/s and 1.7M context.
What context window can stablelm 2 zephyr 1.6b use on RTX 4090 24GB?
On RTX 4090 24GB, stablelm 2 zephyr 1.6b can safely use up to 1.7M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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