RTX 5070 12GBコスパ重視12 GB VRAM (+2)
B
Raises estimated decode speed by about 27%.106.6 tok/sデコード
Raises estimated decode speed by about 27%.
〜$549 MSRP
Can Neural Chat 7B run on RTX 3080 10GB?
YES — Runs Great
B56Good
Estimated from fit model
Neural Chat 7B needs ~8.1 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~84 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 8.1 GB, 84.0 tok/s, Runs well
8.1 GB required10.0 GB available
81% VRAM used
Fit status
Runs well
Decode
84.0 tok/s
TTFT
2305 ms
Safe context
8K
Memory
8.1 GB / 10.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsNeural Chat 7B on RTX 3080 10GB
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: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 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 | B | Runs well | 84.0 tok/s | 1257 ms | 8K |
| Coding | B | Runs well | 84.0 tok/s | 2305 ms | 8K |
| Agentic Coding | C | Runs with offload (needs ~0 GB host RAM) | 84.0 tok/s | 3352 ms | 8K |
| Reasoning | B | Runs well | 84.0 tok/s | 2724 ms | 8K |
| RAG | C | Runs with offload (needs ~0 GB host RAM) | 84.0 tok/s | 4190 ms | 8K |
Quantization options
How Neural Chat 7B (7B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C50 |
Q3_K_S | 3 | 3.4 GB | Low | C51 |
NVFP4 | 4 | 3.9 GB | Medium | C52 |
Q4_K_M | 4 | 4.3 GB | Medium | C52 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
Q6_KBest for your GPU | 6 | 5.7 GB | High | C52 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Neural Chat 7B on your machine.
Run
ollama run neural-chatアップグレードオプション
Neural Chat 7Bを快適に動かすハードウェア
Frequently asked questions
Can RTX 3080 10GB run Neural Chat 7B?
Yes, RTX 3080 10GB can run Neural Chat 7B with a B grade (Runs well). Expected decode speed: 84.0 tok/s.
How much VRAM does Neural Chat 7B need?
Neural Chat 7B (7B parameters) requires approximately 8.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Neural Chat 7B?
The recommended quantization for Neural Chat 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Neural Chat 7B run at on RTX 3080 10GB?
On RTX 3080 10GB, Neural Chat 7B achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.
Can RTX 3080 10GB run Neural Chat 7B for coding?
For coding workloads, Neural Chat 7B on RTX 3080 10GB receives a B grade with 84.0 tok/s and 8K context.
What context window can Neural Chat 7B use on RTX 3080 10GB?
On RTX 3080 10GB, Neural Chat 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/neural-chat-7b-on-rtx-3080-10gb" 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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