RTX 3060 12GBコスパ重視12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.48.7 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can llava llama 3 8b v1 1 run on RTX 3070 Ti 8GB?
YES — With Offload
C54Usable
Estimated from fit model
llava llama 3 8b v1 1 needs ~7.8 GB VRAM. RTX 3070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~90 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: 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, 89.7 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
89.7 tok/s
TTFT
2158 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 feelsllava llama 3 8b v1 1 on RTX 3070 Ti 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: 89.7 tok/s decode · 2.2s TTFT (warm) · 224 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.
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 | 89.7 tok/s | 1177 ms | 19K |
| Coding | C | Runs with offload | 89.7 tok/s | 2158 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 55.6 tok/s | 5061 ms | 19K |
| Reasoning | C | Runs with offload | 89.7 tok/s | 2551 ms | 19K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 55.6 tok/s | 6326 ms | 19K |
Quantization options
How llava llama 3 8b v1 1 (8B params) fits at each quantization level on RTX 3070 Ti 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 llava llama 3 8b v1 1 on your machine.
Run
lms load hf-xtuner--llava-llama-3-8b-v1-1-gguf && lms server startアップグレードオプション
llava llama 3 8b v1 1を快適に動かすハードウェア
RTX 3060 12GBコスパ重視RTX 5070 12GBコスパ最良12 GB VRAM (+4)672 GB/s (+64)
B
Adds memory headroom for longer context windows and future model growth.86.8 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$549 MSRP
RTX 4070 Super 12GBNVIDIAアップグレード12 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.79.5 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$599 MSRP
Frequently asked questions
Can RTX 3070 Ti 8GB run llava llama 3 8b v1 1?
Yes, RTX 3070 Ti 8GB can run llava llama 3 8b v1 1 with a C grade (Runs with offload). Expected decode speed: 89.7 tok/s.
How much VRAM does llava llama 3 8b v1 1 need?
llava llama 3 8b v1 1 (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.
What is the best quantization for llava llama 3 8b v1 1?
The recommended quantization for llava llama 3 8b v1 1 is Q4_K_M, which balances quality and memory efficiency.
What speed will llava llama 3 8b v1 1 run at on RTX 3070 Ti 8GB?
On RTX 3070 Ti 8GB, llava llama 3 8b v1 1 achieves approximately 89.7 tokens per second decode speed with a time-to-first-token of 2158ms using Q4_K_M quantization.
Can RTX 3070 Ti 8GB run llava llama 3 8b v1 1 for coding?
For coding workloads, llava llama 3 8b v1 1 on RTX 3070 Ti 8GB receives a C grade with 89.7 tok/s and 19K context.
What context window can llava llama 3 8b v1 1 use on RTX 3070 Ti 8GB?
On RTX 3070 Ti 8GB, llava llama 3 8b v1 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 llava llama 3 8b v1 1 feels slow on RTX 3070 Ti 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-xtuner--llava-llama-3-8b-v1-1-gguf-on-rtx-3070-ti-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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