Can LLaVA 1.5 7B run on RTX 4070 Ti Super 16GB?

YES — Tight Fit

A72Great

Estimated from fit model

LLaVA 1.5 7B needs ~14.9 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.9 GB, 98.0 tok/s, Tight fit

14.9 GB required16.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

Memory

14.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsLLaVA 1.5 7B on RTX 4070 Ti Super 16GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	A	Runs well	98.0 tok/s	1078 ms	4K
Coding	A	Tight fit	98.0 tok/s	1976 ms	4K
Agentic Coding	F	Too heavy	45.2 tok/s	6224 ms	4K
Reasoning	A	Tight fit	98.0 tok/s	2335 ms	4K
RAG	F	Too heavy	45.2 tok/s	7781 ms	4K

Inference speed

LLaVA 1.5 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for LLaVA 1.5 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Tight
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	43.0	Heavy offload
RTX 3060 12GB	12 GB	Q4_K_M	24.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	10.6	Too big

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 LLaVA 1.5 7B (7B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B65
Q3_K_S	3	3.4 GB	Low	B65
NVFP4	4	3.9 GB	Medium	B66
Q4_K_M	4	4.3 GB	Medium	B66
Q5_K_M	5	5.0 GB	High	B67
Q6_K	6	5.7 GB	High	B67
Q8_0Best for your GPU	8	7.5 GB	Very High	B69
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run LLaVA 1.5 7B on your machine.

Run

ollama run llava

Your hardware

More models your RTX 4070 Ti Super 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	105.3 tok/s
Qwen 3 14B	14B	S	68 tok/s
Qwen 3 8B	8B	S	112 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	64.4 tok/s
GPT-OSS 20B	21B	A	60 tok/s

Frequently asked questions

Can RTX 4070 Ti Super 16GB run LLaVA 1.5 7B?

Yes, RTX 4070 Ti Super 16GB can run LLaVA 1.5 7B with a A grade (Tight fit). Expected decode speed: 98.0 tok/s.

How much VRAM does LLaVA 1.5 7B need?

LLaVA 1.5 7B (7B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.

What is the best quantization for LLaVA 1.5 7B?

The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will LLaVA 1.5 7B run at on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, LLaVA 1.5 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RTX 4070 Ti Super 16GB run LLaVA 1.5 7B for coding?

For coding workloads, LLaVA 1.5 7B on RTX 4070 Ti Super 16GB receives a A grade with 98.0 tok/s and 4K context.

What context window can LLaVA 1.5 7B use on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, LLaVA 1.5 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if LLaVA 1.5 7B feels slow on RTX 4070 Ti Super 16GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4070 Ti Super 16GB See all hardware for LLaVA 1.5 7B

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