Will It Run AI

Can Llama 3.1 8B run on RTX 2070 Super 8GB?

YES — With Offload

B62Good
Estimated from fit model

Llama 3.1 8B needs ~8.5 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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) — 8.5 GB, 38.1 tok/s, Runs with offload (needs ~0.3 GB host RAM)
8.5 GB required8.0 GB available
106% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

38.1 tok/s

TTFT

5080 ms

Safe context

12K

Memory

8.5 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsLlama 3.1 8B on RTX 2070 Super 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: 38.1 tok/s decode · 5.1s TTFT (warm) · 95 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatATight fit60.2 tok/s1754 ms12K
CodingBRuns with offload (needs ~0.3 GB host RAM)38.1 tok/s5080 ms12K
Agentic CodingFToo heavy24.2 tok/s11619 ms12K
ReasoningBRuns with offload (needs ~0.3 GB host RAM)38.1 tok/s6004 ms12K
RAGFToo heavy24.2 tok/s14523 ms12K

Quantization options

How Llama 3.1 8B (8B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowA75
Q3_K_S
3
3.9 GB
LowA74
NVFP4
4
4.5 GB
MediumA74
Q4_K_MBest for your GPU
4
4.9 GB
MediumA74
Q5_K_M
5
5.8 GB
HighF0
Q6_K
6
6.6 GB
HighF0
Q8_0
8
8.6 GB
Very HighF0
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run Llama 3.1 8B on your machine.

Run

ollama run llama3.1

Opções de upgrade

Hardware que roda bem Llama 3.1 8B

NVIDIARTX 3060 12GBOpção econômica
12 GB VRAM (+4)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.44 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

NVIDIARTX 5060 Ti 16GBMelhor custo-benefício
16 GB VRAM (+8)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.61.2 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 61%.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBUpgrade NVIDIA
16 GB VRAM (+8)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.42.6 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

Frequently asked questions

Can RTX 2070 Super 8GB run Llama 3.1 8B?

Yes, RTX 2070 Super 8GB can run Llama 3.1 8B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 38.1 tok/s.

How much VRAM does Llama 3.1 8B need?

Llama 3.1 8B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 8B?

The recommended quantization for Llama 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 8B run at on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, Llama 3.1 8B achieves approximately 38.1 tokens per second decode speed with a time-to-first-token of 5080ms using Q4_K_M quantization.

Can RTX 2070 Super 8GB run Llama 3.1 8B for coding?

For coding workloads, Llama 3.1 8B on RTX 2070 Super 8GB receives a B grade with 38.1 tok/s and 12K context.

What context window can Llama 3.1 8B use on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, Llama 3.1 8B can safely use up to 12K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.1 8B feels slow on RTX 2070 Super 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 2070 Super 8GBSee all hardware for Llama 3.1 8B
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