Will It Run AI

Can Llama 3 8B Instruct 32k v0.1 run on GTX 1070 Ti 8GB?

YES — With Offload

C51Usable
Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~7.8 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Balanced
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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) — 7.8 GB, 31.0 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used

Fit status

Runs with offload

Decode

31.0 tok/s

TTFT

6255 ms

Safe context

19K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsLlama 3 8B Instruct 32k v0.1 on GTX 1070 Ti 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: 31.0 tok/s decode · 6.3s TTFT (warm) · 77 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.

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

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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit31.0 tok/s3412 ms19K
CodingCRuns with offload31.0 tok/s6255 ms19K
Agentic CodingDVery compromised (needs ~0.4 GB host RAM)18.5 tok/s15205 ms19K
ReasoningCRuns with offload31.0 tok/s7392 ms19K
RAGDVery compromised (needs ~0.4 GB host RAM)18.5 tok/s19007 ms19K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC54
Q3_K_S
3
3.9 GB
LowC54
NVFP4
4
4.5 GB
MediumC53
Q4_K_MBest for your GPU
4
4.9 GB
MediumC53
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 8B Instruct 32k v0.1 on your machine.

Run

lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Llama 3 8B Instruct 32k v0.1

NVIDIARTX 3060 12GBOpción económica
12 GB VRAM (+4)360 GB/s (+104)
C
Sube la velocidad estimada de decodificación alrededor de un 57%.48.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 57%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$329 MSRP

NVIDIARTX 5060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+8)448 GB/s (+192)
C
Sube la velocidad estimada de decodificación alrededor de un 84%.56.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 84%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejora NVIDIA
16 GB VRAM (+8)288 GB/s (+32)
C
Sube la velocidad estimada de decodificación alrededor de un 39%.43.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 39%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run Llama 3 8B Instruct 32k v0.1?

Yes, GTX 1070 Ti 8GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs with offload). Expected decode speed: 31.0 tok/s.

How much VRAM does Llama 3 8B Instruct 32k v0.1 need?

Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3 8B Instruct 32k v0.1?

The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3 8B Instruct 32k v0.1 run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 31.0 tokens per second decode speed with a time-to-first-token of 6255ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on GTX 1070 Ti 8GB receives a C grade with 31.0 tok/s and 19K context.

What context window can Llama 3 8B Instruct 32k v0.1 use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3 8B Instruct 32k v0.1 feels slow on GTX 1070 Ti 8GB?

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 GTX 1070 Ti 8GBSee all hardware for Llama 3 8B Instruct 32k v0.1
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