Will It Run AI

Can SOLAR 10.7B v1.0 run on GTX 1070 Ti 8GB?

BARELY — Tight on Memory

D31Poor
Estimated from fit model

SOLAR 10.7B v1.0 needs ~9.5 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~12 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) — 9.5 GB, 11.6 tok/s, Very compromised (needs ~1 GB host RAM)
9.5 GB required8.0 GB available
119% VRAM needed

1.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

11.6 tok/s

TTFT

16654 ms

Safe context

4K

Memory

9.5 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B v1.0 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: 11.6 tok/s decode · 16.7s TTFT (warm) · 29 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 20% 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 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatDVery compromised (needs ~0.6 GB host RAM)13.5 tok/s7817 ms4K
CodingDVery compromised (needs ~1 GB host RAM)11.6 tok/s16654 ms4K
Agentic CodingFToo heavy8.8 tok/s31821 ms4K
ReasoningDVery compromised (needs ~1 GB host RAM)11.6 tok/s19683 ms4K
RAGFToo heavy8.8 tok/s39777 ms4K

Quantization options

How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowC53
Q3_K_SBest for your GPU
3
5.2 GB
LowC52
NVFP4
4
6.0 GB
MediumF0
Q4_K_M
4
6.5 GB
MediumF0
Q5_K_M
5
7.7 GB
HighF0
Q6_K
6
8.8 GB
HighF0
Q8_0
8
11.4 GB
Very HighF0
F16
16
21.9 GB
MaximumF0

Get started

Copy-paste commands to run SOLAR 10.7B v1.0 on your machine.

Run

lms load hf-mradermacher--solar-10-7b-v1-0-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien SOLAR 10.7B v1.0

NVIDIARTX 3060 12GBOpción económica
12 GB VRAM (+4)360 GB/s (+104)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.35.7 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 208%.

~$329 MSRP

NVIDIARTX 5060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+8)448 GB/s (+192)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.41.3 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 256%.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejora NVIDIA
16 GB VRAM (+8)288 GB/s (+32)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.33.8 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 191%.

~$499 MSRP

Frequently asked questions

Can GTX 1070 Ti 8GB run SOLAR 10.7B v1.0?

Yes, GTX 1070 Ti 8GB can run SOLAR 10.7B v1.0 with a D grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 11.6 tok/s.

How much VRAM does SOLAR 10.7B v1.0 need?

SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.

What is the best quantization for SOLAR 10.7B v1.0?

The recommended quantization for SOLAR 10.7B v1.0 is Q4_K_M, which balances quality and memory efficiency.

What speed will SOLAR 10.7B v1.0 run at on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, SOLAR 10.7B v1.0 achieves approximately 11.6 tokens per second decode speed with a time-to-first-token of 16654ms using Q4_K_M quantization.

Can GTX 1070 Ti 8GB run SOLAR 10.7B v1.0 for coding?

For coding workloads, SOLAR 10.7B v1.0 on GTX 1070 Ti 8GB receives a D grade with 11.6 tok/s and 4K context.

What context window can SOLAR 10.7B v1.0 use on GTX 1070 Ti 8GB?

On GTX 1070 Ti 8GB, SOLAR 10.7B v1.0 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if SOLAR 10.7B v1.0 feels slow on GTX 1070 Ti 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 GTX 1070 Ti 8GBSee all hardware for SOLAR 10.7B v1.0
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