RTX 5090 32GBOpção econômica32 GB VRAM (+8)1792 GB/s (+784)
C
Adds memory headroom for longer context windows and future model growth.28.5 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Qwen2.5 1.5B Instruct run on RTX 4090 24GB?
YES — Runs Great
C41Usable
Estimated from fit model
Qwen2.5 1.5B Instruct needs ~4.4 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~21 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) — 4.4 GB, 24.0 tok/s, Runs well
4.4 GB required24.0 GB available
18% VRAM used
Fit status
Runs well
Decode
24.0 tok/s
TTFT
8067 ms
Safe context
1.8M
Memory
4.4 GB / 24.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsQwen2.5 1.5B Instruct 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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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 | 24.0 tok/s | 4400 ms | 1.6M |
| Coding | C | Runs well | 21.0 tok/s | 9219 ms | 1.8M |
| Agentic Coding | C | Runs well | 24.0 tok/s | 11733 ms | 1.8M |
| Reasoning | C | Runs well | 24.0 tok/s | 9533 ms | 1.8M |
| RAG | C | Runs well | 24.0 tok/s | 14667 ms | 1.8M |
Quantization options
How Qwen2.5 1.5B Instruct (1.5B 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 | C44 |
Q3_K_S | 3 | 0.7 GB | Low | C44 |
NVFP4 | 4 | 0.8 GB | Medium | C44 |
Q4_K_M | 4 | 0.9 GB | Medium | C44 |
Q5_K_M | 5 | 1.1 GB | High | C44 |
Q6_K | 6 | 1.2 GB | High | C44 |
Q8_0 | 8 | 1.6 GB | Very High | C44 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | C45 |
Get started
Copy-paste commands to run Qwen2.5 1.5B Instruct on your machine.
Run
lms load hf-qwen--qwen2-5-1-5b-instruct-gguf && lms server startOpções de upgrade
Hardware que roda bem Qwen2.5 1.5B Instruct
Frequently asked questions
Can RTX 4090 24GB run Qwen2.5 1.5B Instruct?
Yes, RTX 4090 24GB can run Qwen2.5 1.5B Instruct with a C grade (Runs well). Expected decode speed: 21.0 tok/s.
How much VRAM does Qwen2.5 1.5B Instruct need?
Qwen2.5 1.5B Instruct (1.5B parameters) requires approximately 4.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen2.5 1.5B Instruct?
The recommended quantization for Qwen2.5 1.5B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen2.5 1.5B Instruct run at on RTX 4090 24GB?
On RTX 4090 24GB, Qwen2.5 1.5B Instruct achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.
Can RTX 4090 24GB run Qwen2.5 1.5B Instruct for coding?
For coding workloads, Qwen2.5 1.5B Instruct on RTX 4090 24GB receives a C grade with 21.0 tok/s and 1.8M context.
What context window can Qwen2.5 1.5B Instruct use on RTX 4090 24GB?
On RTX 4090 24GB, Qwen2.5 1.5B Instruct can safely use up to 1.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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