MacBook Pro M4 16GBOpção econômica
16 GB Unified (+5)
C
This setup is broadly balanced for this model.15.4 tok/s decodificação
~$599 MSRP
Can TinyLlama 1.1B Chat v1.0 imatrix run on RTX 2080 Ti 11GB?
YES — Runs Great
C42Usable
Estimated from fit model
TinyLlama 1.1B Chat v1.0 imatrix needs ~3.1 GB VRAM. RTX 2080 Ti 11GB has 11.0 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) — 3.1 GB, 15.4 tok/s, Runs well
3.1 GB required11.0 GB available
28% VRAM used
Fit status
Runs well
Decode
15.4 tok/s
TTFT
12571 ms
Safe context
997K
Memory
3.1 GB / 11.0 GB
Memory breakdown
Weights0.7 GB
KV Cache0.1 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsTinyLlama 1.1B Chat v1.0 imatrix on RTX 2080 Ti 11GB
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.
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
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 15.4 tok/s | 6857 ms | 642K |
| Coding | C | Runs well | 15.4 tok/s | 12571 ms | 997K |
| Agentic Coding | C | Runs well | 15.4 tok/s | 18286 ms | 997K |
| Reasoning | C | Runs well | 15.4 tok/s | 14857 ms | 997K |
| RAG | C | Runs well | 15.4 tok/s | 22857 ms | 997K |
Quantization options
How TinyLlama 1.1B Chat v1.0 imatrix (1.100000023841858B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | C47 |
Q3_K_S | 3 | 0.5 GB | Low | C47 |
NVFP4 | 4 | 0.6 GB | Medium | C47 |
Q4_K_M | 4 | 0.7 GB | Medium | C47 |
Q5_K_M | 5 | 0.8 GB | High | C47 |
Q6_K | 6 | 0.9 GB | High | C47 |
Q8_0 | 8 | 1.2 GB | Very High | C47 |
F16Best for your GPU | 16 | 2.3 GB | Maximum | C49 |
Get started
Copy-paste commands to run TinyLlama 1.1B Chat v1.0 imatrix on your machine.
Run
lms load hf-duyntnet--tinyllama-1-1b-chat-v1-0-imatrix-gguf && lms server startOpções de upgrade
Hardware que roda bem TinyLlama 1.1B Chat v1.0 imatrix
Frequently asked questions
Can RTX 2080 Ti 11GB run TinyLlama 1.1B Chat v1.0 imatrix?
Yes, RTX 2080 Ti 11GB can run TinyLlama 1.1B Chat v1.0 imatrix with a C grade (Runs well). Expected decode speed: 15.4 tok/s.
How much VRAM does TinyLlama 1.1B Chat v1.0 imatrix need?
TinyLlama 1.1B Chat v1.0 imatrix (1.100000023841858B parameters) requires approximately 3.1 GB of memory with Q4_K_M quantization.
What is the best quantization for TinyLlama 1.1B Chat v1.0 imatrix?
The recommended quantization for TinyLlama 1.1B Chat v1.0 imatrix is Q4_K_M, which balances quality and memory efficiency.
What speed will TinyLlama 1.1B Chat v1.0 imatrix run at on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, TinyLlama 1.1B Chat v1.0 imatrix achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run TinyLlama 1.1B Chat v1.0 imatrix for coding?
For coding workloads, TinyLlama 1.1B Chat v1.0 imatrix on RTX 2080 Ti 11GB receives a C grade with 15.4 tok/s and 997K context.
What context window can TinyLlama 1.1B Chat v1.0 imatrix use on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, TinyLlama 1.1B Chat v1.0 imatrix can safely use up to 997K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-duyntnet--tinyllama-1-1b-chat-v1-0-imatrix-gguf-on-rtx-2080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: