Can TinyLlama 1.1B run on RTX 4070 Laptop 8GB?

YES — Runs Great

B56Good

Estimated from fit model

TinyLlama 1.1B needs ~3.0 GB VRAM. RTX 4070 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 3.0 GB, 15.4 tok/s, Runs well

3.0 GB required8.0 GB available

38% VRAM used

Fit status

Runs well

Decode

15.4 tok/s

TTFT

12571 ms

Safe context

Memory

3.0 GB / 8.0 GB

Memory breakdown

Weights0.7 GB

KV Cache0.3 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsTinyLlama 1.1B on RTX 4070 Laptop 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: 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	B	Runs well	15.4 tok/s	6857 ms	4K
Coding	B	Runs well	15.4 tok/s	12571 ms	4K
Agentic Coding	B	Runs well	15.4 tok/s	18286 ms	4K
Reasoning	B	Runs well	15.4 tok/s	14857 ms	4K
RAG	B	Runs well	15.4 tok/s	22857 ms	4K

Inference speed

TinyLlama 1.1B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for TinyLlama 1.1B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~21 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	20.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	17.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	17.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	15.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	15.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	15.4	Fits
RTX 4060 8GB	8 GB	Q4_K_M	15.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	15.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	15.4	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	15.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	15.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	15.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	15.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	15.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	15.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	15.4	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How TinyLlama 1.1B (1.100000023841858B params) fits at each quantization level on RTX 4070 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.4 GB	Low	B61
Q3_K_S	3	0.5 GB	Low	B61
NVFP4	4	0.6 GB	Medium	B61
Q4_K_M	4	0.7 GB	Medium	B61
Q5_K_M	5	0.8 GB	High	B61
Q6_K	6	0.9 GB	High	B61
Q8_0	8	1.2 GB	Very High	B62
F16Best for your GPU	16	2.3 GB	Maximum	B64

Get started

Copy-paste commands to run TinyLlama 1.1B on your machine.

Run

ollama run tinyllama

Frequently asked questions

Can RTX 4070 Laptop 8GB run TinyLlama 1.1B?

Yes, RTX 4070 Laptop 8GB can run TinyLlama 1.1B with a B 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 3.0 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 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, 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 4070 Laptop 8GB run TinyLlama 1.1B for coding?

For coding workloads, TinyLlama 1.1B on RTX 4070 Laptop 8GB receives a B grade with 15.4 tok/s and 4K context.

What context window can TinyLlama 1.1B use on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, 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.

See all results for RTX 4070 Laptop 8GB See all hardware for TinyLlama 1.1B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/tinyllama-1.1b-on-rtx-4070-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

15.4 tok/s

6857 ms

Coding

Runs well

15.4 tok/s

12571 ms

Agentic Coding

Runs well

15.4 tok/s

18286 ms

Reasoning

Runs well

15.4 tok/s

14857 ms

RAG

Runs well

15.4 tok/s

22857 ms

TinyLlama 1.1B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

20.9

Fits

RTX 4090 24GB

24 GB

Q4_K_M

17.6

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

17.6

Fits

RTX 3090 24GB

24 GB

Q4_K_M

15.4

Fits

RTX 4070 12GB

12 GB

Q4_K_M

15.4

Fits

RTX 3060 12GB

12 GB

Q4_K_M

15.4

Fits

RTX 4060 8GB

8 GB

Q4_K_M

15.4

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

15.4

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

15.4

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

15.4

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

15.4

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

15.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

15.4

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

15.4

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

15.4

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

15.4

Fits

Quantization options

How TinyLlama 1.1B (1.100000023841858B params) fits at each quantization level on RTX 4070 Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.4 GB	Low	B61
Q3_K_S	3	0.5 GB	Low	B61
NVFP4	4	0.6 GB	Medium	B61
Q4_K_M	4	0.7 GB	Medium	B61
Q5_K_M	5	0.8 GB	High	B61
Q6_K	6	0.9 GB	High	B61
Q8_0	8	1.2 GB	Very High	B62
F16Best for your GPU	16	2.3 GB	Maximum	B64

Frequently asked questions

Can RTX 4070 Laptop 8GB run TinyLlama 1.1B?

Yes, RTX 4070 Laptop 8GB can run TinyLlama 1.1B with a B 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 3.0 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 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, 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 4070 Laptop 8GB run TinyLlama 1.1B for coding?

For coding workloads, TinyLlama 1.1B on RTX 4070 Laptop 8GB receives a B grade with 15.4 tok/s and 4K context.

What context window can TinyLlama 1.1B use on RTX 4070 Laptop 8GB?

On RTX 4070 Laptop 8GB, 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.