Spark-X2.6 1.7B VRAM Requirements

Spark-X2.6 1.7B at a glance

Made by

iFlytek, China

Released

October 27, 2026

Parameters

1.71B

Context window

1M tokens

License

Apache 2.0, commercial use allowed

On Hugging Face

72 likes, 2.7K downloads last month (opens in a new tab)

On ModelScope

XHToken/Spark-X2.6-1.7B (opens in a new tab)

Runs with

vLLM, SGLang, llama.cpp (Ollama, LM Studio), MLX (Mac)

vLLM needs the extra setup described on the model card (model card (opens in a new tab))

VRAM by precision and context length

Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.

Spark-X2.6 1.7B GGUF files

Exact size of each quantization in mradermacher/Spark-X2.6-1.7B-i1-GGUF (opens in a new tab), and the smallest GPU that runs it with llama.cpp, Ollama or LM Studio and an 8K-token context.

Read every day from Hugging Face. Split files are added together; vision projectors are left out.

How many GPUs to run Spark-X2.6 1.7B

Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.

8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size.

Cheapest way to run Spark-X2.6 1.7B today

For each GPU: the fewest cards that fit Spark-X2.6 1.7B with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-11-03).

On-demand prices from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.

AWS, Google Cloud and UpCloud prices: Spare Cores (opens in a new tab), CC BY-SA 4.0 (opens in a new tab), shared under the same license.

KV cache: how memory grows with context

Architecture: 28 layers · 7 full attention (2 KV × 256) · 21 sliding-window layers (512 tokens, 2 KV × 256). Its KV cache is 3.8× smaller than if every layer used full attention, because most layers only keep a short window of recent tokens.

Spark-X2.6 1.7B VRAM badge

For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/spark-x2-6-1-7b.json.

[![Spark-X2.6 1.7B VRAM](https://studiotvai.com/badge/spark-x2-6-1-7b.svg)](https://studiotvai.com/vram-requirements/spark-x2-6-1-7b)

Other models

Same family (sliding-window attention): Gemma 2 9B · Gemma 3 4B · Unlimited-OCR · Gemma 4 12B · Gemma 4 31B · Gemma 4 26B-A4B · gpt-oss 20B · gpt-oss 120B · Llama 4 Scout 17B-16E · Llama 4 Maverick 17B-128E · Kolibri-1 · Recursive Flint · Nemotron Labs Dory 1.0 4B · Step 3.5 Flash · Step 3.7 Flash · Laguna S 2.1 · Laguna XS 2.1 · Command A+ · Inkling Small · Muse Glimmer 30B · Humanizer · Mellum2.1 12B-A2.5B · Spark-X2.5 4B · Ministral 8B · Gemma 4 E4B · Gemma 4 E2B · Spark-X2.6 4B

Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.