Can SOLAR 10.7B v1.0 run on GTX 1660 Super 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

SOLAR 10.7B v1.0 needs ~9.6 GB but GTX 1660 Super 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 9.6 GB, exceeds 6.0 GB available

9.6 GB required6.0 GB available

160% VRAM needed

3.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.4 tok/s

TTFT

26199 ms

Safe context

Memory

9.6 GB / 6.0 GB

Offload

40%

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsSOLAR 10.7B v1.0 on GTX 1660 Super 6GB
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: 7.4 tok/s decode · 26.2s TTFT (warm) · 19 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 9.6 GB, but this setup only exposes 6.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	8.6 tok/s	12317 ms	4K
Coding	F	Too heavy	7.4 tok/s	26199 ms	4K
Agentic Coding	F	Too heavy	5.6 tok/s	49925 ms	4K
Reasoning	F	Too heavy	7.4 tok/s	30963 ms	4K
RAG	F	Too heavy	5.6 tok/s	62406 ms	4K

Inference speed

SOLAR 10.7B v1.0 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for SOLAR 10.7B v1.0 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~150 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	149.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	117.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	105.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	100.4	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	93.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	85.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	71.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	67.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	60.8	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	46.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	46.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	36.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	36.4	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	33.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	28.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	16.8	Heavy offload

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 SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	F0
Q3_K_S	3	5.2 GB	Low	F0
NVFP4	4	6.0 GB	Medium	F0
Q4_K_M	4	6.5 GB	Medium	F0
Q5_K_M	5	7.7 GB	High	F0
Q6_K	6	8.8 GB	High	F0
Q8_0	8	11.4 GB	Very High	F0
F16	16	21.9 GB	Maximum	F0

升级选项

能流畅运行 SOLAR 10.7B v1.0 的硬件

RTX 3060 12GB经济之选

12 GB VRAM (+6)360 GB/s (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.36.4 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$329 MSRP

RTX 5060 Ti 16GB最佳性价比

16 GB VRAM (+10)448 GB/s (+112)

Makes the model fit on the accelerator instead of staying completely out of reach.42.6 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA 升级

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.32.2 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$499 MSRP

Frequently asked questions

Can GTX 1660 Super 6GB run SOLAR 10.7B v1.0?

No, SOLAR 10.7B v1.0 requires more memory than GTX 1660 Super 6GB provides.

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

SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 9.6 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 1660 Super 6GB?

On GTX 1660 Super 6GB, SOLAR 10.7B v1.0 achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26199ms using Q4_K_M quantization.

Can GTX 1660 Super 6GB run SOLAR 10.7B v1.0 for coding?

For coding workloads, SOLAR 10.7B v1.0 on GTX 1660 Super 6GB receives a F grade with 7.4 tok/s and 4K context.

What context window can SOLAR 10.7B v1.0 use on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, 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 1660 Super 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1660 Super 6GB See all hardware for SOLAR 10.7B v1.0

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