RTX 3090 24GBOpción económica24 GB VRAM (+4)936 GB/s (+576)
C
Sube la velocidad estimada de decodificación alrededor de un 95%.112 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 95%.
~$1,499 MSRP
Can Llama 3 8B Instruct 32k v0.1 run on RTX 4000 Ada 20GB?
YES — Runs Great
C50Usable
Estimated from fit model
Llama 3 8B Instruct 32k v0.1 needs ~9.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~58 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) — 9.0 GB, 57.5 tok/s, Runs well
9.0 GB required20.0 GB available
45% VRAM used
Fit status
Runs well
Decode
57.5 tok/s
TTFT
3365 ms
Safe context
203K
Memory
9.0 GB / 20.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsLlama 3 8B Instruct 32k v0.1 on RTX 4000 Ada 20GB
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: 57.5 tok/s decode · 3.4s TTFT (warm) · 144 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 | 57.5 tok/s | 1835 ms | 203K |
| Coding | C | Runs well | 57.5 tok/s | 3365 ms | 203K |
| Agentic Coding | C | Runs well | 57.5 tok/s | 4894 ms | 203K |
| Reasoning | C | Runs well | 57.5 tok/s | 3976 ms | 203K |
| RAG | C | Runs well | 57.5 tok/s | 6117 ms | 203K |
Quantization options
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C46 |
Q3_K_S | 3 | 3.9 GB | Low | C46 |
NVFP4 | 4 | 4.5 GB | Medium | C47 |
Q4_K_M | 4 | 4.9 GB | Medium | C47 |
Q5_K_M | 5 | 5.8 GB | High | C48 |
Q6_K | 6 | 6.6 GB | High | C48 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C50 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.
Run
lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server startOpciones de mejora
Hardware que ejecuta bien Llama 3 8B Instruct 32k v0.1
RTX 3090 24GBOpción económicaRTX 4090 24GBMejor relación calidad-precio24 GB VRAM (+4)1008 GB/s (+648)
C
Sube la velocidad estimada de decodificación alrededor de un 95%.112 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 95%.
~$1,599 MSRP
RTX PRO 4000 Blackwell 24GBMejora NVIDIA24 GB VRAM (+4)672 GB/s (+312)
C
Sube la velocidad estimada de decodificación alrededor de un 95%.112 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 95%.
~$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run Llama 3 8B Instruct 32k v0.1?
Yes, RTX 4000 Ada 20GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs well). Expected decode speed: 57.5 tok/s.
How much VRAM does Llama 3 8B Instruct 32k v0.1 need?
Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Llama 3 8B Instruct 32k v0.1?
The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Llama 3 8B Instruct 32k v0.1 run at on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 57.5 tokens per second decode speed with a time-to-first-token of 3365ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run Llama 3 8B Instruct 32k v0.1 for coding?
For coding workloads, Llama 3 8B Instruct 32k v0.1 on RTX 4000 Ada 20GB receives a C grade with 57.5 tok/s and 203K context.
What context window can Llama 3 8B Instruct 32k v0.1 use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 203K 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-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf-on-rtx-4000-ada-20gb" 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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