RTX 3060 12GBGünstige Wahl12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.48.7 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $329 MSRP
Can Llama 3 8B Instruct 32k v0.1 run on RTX 2060 Super 8GB?
YES — With Offload
C52Usable
Estimated from fit model
Llama 3 8B Instruct 32k v0.1 needs ~7.8 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~53 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: 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) — 7.8 GB, 53.2 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
53.2 tok/s
TTFT
3636 ms
Safe context
19K
Memory
7.8 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsLlama 3 8B Instruct 32k v0.1 on RTX 2060 Super 8GB
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: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 53.2 tok/s | 1983 ms | 19K |
| Coding | C | Runs with offload | 53.2 tok/s | 3636 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 31.9 tok/s | 8839 ms | 19K |
| Reasoning | C | Runs with offload | 53.2 tok/s | 4297 ms | 19K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 31.9 tok/s | 11048 ms | 19K |
Quantization options
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C54 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
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 startUpgrade-Optionen
Hardware, die Llama 3 8B Instruct 32k v0.1 gut ausführt
RTX 3060 12GBGünstige WahlRTX 5070 12GBBestes Preis-Leistungs-Verhältnis12 GB VRAM (+4)672 GB/s (+224)
B
Raises estimated decode speed by about 63%.86.8 tok/s Dekodierung
Raises estimated decode speed by about 63%.
Adds memory headroom for longer context windows and future model growth.
ca. $549 MSRP
RTX 4070 Super 12GBNVIDIA-Upgrade12 GB VRAM (+4)504 GB/s (+56)
B
Raises estimated decode speed by about 49%.79.5 tok/s Dekodierung
Raises estimated decode speed by about 49%.
Adds memory headroom for longer context windows and future model growth.
ca. $599 MSRP
Frequently asked questions
Can RTX 2060 Super 8GB run Llama 3 8B Instruct 32k v0.1?
Yes, RTX 2060 Super 8GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs with offload). Expected decode speed: 53.2 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 7.8 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 2060 Super 8GB?
On RTX 2060 Super 8GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3636ms using Q4_K_M quantization.
Can RTX 2060 Super 8GB run Llama 3 8B Instruct 32k v0.1 for coding?
For coding workloads, Llama 3 8B Instruct 32k v0.1 on RTX 2060 Super 8GB receives a C grade with 53.2 tok/s and 19K context.
What context window can Llama 3 8B Instruct 32k v0.1 use on RTX 2060 Super 8GB?
On RTX 2060 Super 8GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Llama 3 8B Instruct 32k v0.1 feels slow on RTX 2060 Super 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf-on-rtx-2060-super-8gb" 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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