Can vntl llama3 8b v2 run on RTX 5070 12GB?

YES — Runs Great

B57Good

Estimated from fit model

vntl llama3 8b v2 needs ~8.2 GB VRAM. RTX 5070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~87 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.2 GB, 86.8 tok/s, Runs well

8.2 GB required12.0 GB available

68% VRAM used

Fit status

Runs well

Decode

86.8 tok/s

TTFT

2232 ms

Safe context

81K

Memory

8.2 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsvntl llama3 8b v2 on RTX 5070 12GB
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: 86.8 tok/s decode · 2.2s TTFT (warm) · 217 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	86.8 tok/s	1217 ms	81K
Coding	B	Runs well	86.8 tok/s	2232 ms	81K
Agentic Coding	B	Runs well	86.8 tok/s	3246 ms	81K
Reasoning	B	Runs well	86.8 tok/s	2637 ms	81K
RAG	B	Runs well	86.8 tok/s	4057 ms	81K

Inference speed

vntl llama3 8b v2 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for vntl llama3 8b v2 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	95.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	90.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	77.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	76.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	76.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	49.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	48.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	45.1	Fits
RTX 4060 8GB	8 GB	Q4_K_M	40.7	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	39.6	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 vntl llama3 8b v2 (8B params) fits at each quantization level on RTX 5070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C50
Q3_K_S	3	3.9 GB	Low	C51
NVFP4	4	4.5 GB	Medium	C51
Q4_K_M	4	4.9 GB	Medium	C52
Q5_K_M	5	5.8 GB	High	C53
Q6_K	6	6.6 GB	High	C52
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run vntl llama3 8b v2 on your machine.

Run

lms load hf-lmg-anon--vntl-llama3-8b-v2-gguf && lms server start

Frequently asked questions

Can RTX 5070 12GB run vntl llama3 8b v2?

Yes, RTX 5070 12GB can run vntl llama3 8b v2 with a B grade (Runs well). Expected decode speed: 86.8 tok/s.

How much VRAM does vntl llama3 8b v2 need?

vntl llama3 8b v2 (8B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.

What is the best quantization for vntl llama3 8b v2?

The recommended quantization for vntl llama3 8b v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will vntl llama3 8b v2 run at on RTX 5070 12GB?

On RTX 5070 12GB, vntl llama3 8b v2 achieves approximately 86.8 tokens per second decode speed with a time-to-first-token of 2232ms using Q4_K_M quantization.

Can RTX 5070 12GB run vntl llama3 8b v2 for coding?

For coding workloads, vntl llama3 8b v2 on RTX 5070 12GB receives a B grade with 86.8 tok/s and 81K context.

What context window can vntl llama3 8b v2 use on RTX 5070 12GB?

On RTX 5070 12GB, vntl llama3 8b v2 can safely use up to 81K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5070 12GB See all hardware for vntl llama3 8b v2

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