MacBook Pro M4 16GBGünstige Wahl
16 GB Unified (+6)
B
This setup is broadly balanced for this model.15.4 tok/s Dekodierung
ca. $599 MSRP
Can TinyLlama 1.1B run on RTX 3080 10GB?
YES — Runs Great
C55Usable
Estimated from fit model
TinyLlama 1.1B needs ~3.2 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~15 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: 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) — 3.2 GB, 15.4 tok/s, Runs well
3.2 GB required10.0 GB available
32% VRAM used
Fit status
Runs well
Decode
15.4 tok/s
TTFT
12571 ms
Safe context
4K
Memory
3.2 GB / 10.0 GB
Memory breakdown
Weights0.7 GB
KV Cache0.3 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsTinyLlama 1.1B 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: 15.4 tok/s decode · 12.6s TTFT (warm) · 39 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 | 15.4 tok/s | 6857 ms | 4K |
| Coding | C | Runs well | 15.4 tok/s | 12571 ms | 4K |
| Agentic Coding | B | Runs well | 15.4 tok/s | 18286 ms | 4K |
| Reasoning | C | Runs well | 15.4 tok/s | 14857 ms | 4K |
| RAG | B | Runs well | 15.4 tok/s | 22857 ms | 4K |
Quantization options
How TinyLlama 1.1B (1.100000023841858B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | B59 |
Q3_K_S | 3 | 0.5 GB | Low | B59 |
NVFP4 | 4 | 0.6 GB | Medium | B59 |
Q4_K_M | 4 | 0.7 GB | Medium | B59 |
Q5_K_M | 5 | 0.8 GB | High | B60 |
Q6_K | 6 | 0.9 GB | High | B60 |
Q8_0 | 8 | 1.2 GB | Very High | B60 |
F16Best for your GPU | 16 | 2.3 GB | Maximum | B62 |
Get started
Copy-paste commands to run TinyLlama 1.1B on your machine.
Run
ollama run tinyllamaUpgrade-Optionen
Hardware, die TinyLlama 1.1B gut ausführt
Frequently asked questions
Can RTX 3080 10GB run TinyLlama 1.1B?
Yes, RTX 3080 10GB can run TinyLlama 1.1B with a C grade (Runs well). Expected decode speed: 15.4 tok/s.
How much VRAM does TinyLlama 1.1B need?
TinyLlama 1.1B (1.100000023841858B parameters) requires approximately 3.2 GB of memory with Q4_K_M quantization.
What is the best quantization for TinyLlama 1.1B?
The recommended quantization for TinyLlama 1.1B is Q4_K_M, which balances quality and memory efficiency.
What speed will TinyLlama 1.1B run at on RTX 3080 10GB?
On RTX 3080 10GB, TinyLlama 1.1B achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.
Can RTX 3080 10GB run TinyLlama 1.1B for coding?
For coding workloads, TinyLlama 1.1B on RTX 3080 10GB receives a C grade with 15.4 tok/s and 4K context.
What context window can TinyLlama 1.1B use on RTX 3080 10GB?
On RTX 3080 10GB, TinyLlama 1.1B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/tinyllama-1.1b-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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