Can solar finalised finetuned Model 10.7B i1 run on NVIDIA A16 64GB?

YES — Runs Great

C46Usable

Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~15.4 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~72 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) — 15.4 GB, 71.7 tok/s, Runs well

15.4 GB required64.0 GB available

24% VRAM used

Fit status

Runs well

Decode

71.7 tok/s

TTFT

2700 ms

Safe context

636K

Memory

15.4 GB / 64.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on NVIDIA A16 64GB
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: 71.7 tok/s decode · 2.7s TTFT (warm) · 179 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	C	Runs well	71.7 tok/s	1473 ms	636K
Coding	C	Runs well	71.7 tok/s	2700 ms	636K
Agentic Coding	C	Runs well	71.7 tok/s	3927 ms	636K
Reasoning	C	Runs well	71.7 tok/s	3191 ms	636K
RAG	C	Runs well	71.7 tok/s	4909 ms	636K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C40
Q3_K_S	3	5.2 GB	Low	C40
NVFP4	4

Get started

Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.

Run

lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server start

Upgrade options

Hardware that runs solar finalised finetuned Model 10.7B i1 well

NVIDIA A100 80GBBudget pick

80 GB VRAM (+16)2039 GB/s (+1439)

Raises estimated decode speed by about 109%.149.8 tok/s decode

Raises estimated decode speed by about 109%.

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

~$15,000 MSRP

NVIDIA A800 80GBBest value

80 GB VRAM (+16)1935 GB/s (+1335)

Raises estimated decode speed by about 109%.149.8 tok/s decode

Raises estimated decode speed by about 109%.

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

~$15,000 MSRP

NVIDIA H800 80GBNVIDIA upgrade

80 GB VRAM (+16)3000 GB/s (+2400)

Raises estimated decode speed by about 109%.149.8 tok/s decode

Raises estimated decode speed by about 109%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run solar finalised finetuned Model 10.7B i1?

Yes, NVIDIA A16 64GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Runs well). Expected decode speed: 71.7 tok/s.

How much VRAM does solar finalised finetuned Model 10.7B i1 need?

solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.

What is the best quantization for solar finalised finetuned Model 10.7B i1?

The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will solar finalised finetuned Model 10.7B i1 run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, solar finalised finetuned Model 10.7B i1 achieves approximately 71.7 tokens per second decode speed with a time-to-first-token of 2700ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on NVIDIA A16 64GB receives a C grade with 71.7 tok/s and 636K context.

What context window can solar finalised finetuned Model 10.7B i1 use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, solar finalised finetuned Model 10.7B i1 can safely use up to 636K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for solar finalised finetuned Model 10.7B i1

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