Spark X2.5 1.7B — Hardware Requirements & GPU Compatibility
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Spark X2.5 1.7B is a 1.7B-parameter open language model from XHToken. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 1.44 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 64K)Best on 24 GB (Q4_K_M · ~455 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~50 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Spark X2.5 1.7B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/XHToken/Spark-X2.5-1.7B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.7 GB free · 16K context
Download: Spark-X2.5-1.7B-Q4_K_M.gguf1.11 GBXHToken
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (9 GB headroom) · Q4_K_M
- Generation speed
- ~163 tok/s
- generation speed
- Cost per 1M output tokens
- $0.14
- per 1M output tokens
How Much VRAM Does Spark X2.5 1.7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.1 GB | 2.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~575 tok/s | |
| 1.3 GB | 3 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~524 tok/s | |
| 1.4 GB | 3.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~455 tok/s | |
| 1.6 GB | 3.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~402 tok/s | |
| 1.8 GB | 3.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~358 tok/s | |
| 2.1 GB | 3.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~308 tok/s | |
| 3.8 GB | 5.6 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 256K context · ~171 tok/s |
Selected Q4_K_M
4-bit medium quantization2.3GBat 16K context
- Weights
- 1.0 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.7 GB free · ~455 tok/s
≥50 tok/s needs ≥111 GB/s · RTX 5090 ~809, RTX 3090 Ti ~455
Which Hardware Can Run Spark X2.5 1.7B?
Q4_K_M · 1.4 GBSpark X2.5 1.7B (Q4_K_M) requires 1.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 1049K context window can add up to 60.0 GB, bringing total usage to 61.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Spark X2.5 1.7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.7 GB free · up to 64K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~343Simulate output on RTX 3080RTX 2080 Ti~278Simulate output on RTX 2080 TiRTX 3070 Ti~275Simulate output on RTX 3070 TiM3*8 GB~50Simulate output on iMac 24" M3 (8 GB)Orin Nano~46Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~33Simulate output on iPhone 1720 GB
fits · 17.7 GB free · up to 256K ctx
1 GPU
RX 7900 XT~333Simulate output on RX 7900 XT32–40 GB
fits · 29.7 GB free · up to 512K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~809Simulate output on RTX 5090M5 Max36 GB~224Simulate output on Mac Studio M5 Max (36 GB)M532 GB~75Simulate output on MacBook Air 13" M5 (32 GB)Orin~92Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~61Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite 4.0 1BIBM | — | 2.6 GB | ~452 | same size |
| LFM2 VL 1.6BLiquid AI | — | 1.5 GB | ~496 | same size |
| Qwen 1 8BAlibaba | — | 1.9 GB | ~542 | same size |
| Qwen3 1.7B BaseAlibaba | — | 3.2 GB | ~417 | same size |
| DeepSeek R1 Distill Qwen 1.5BDeepSeek | — | 1.8 GB | ~461 | same size |
| SmolLM2 1.7B InstructHugging Face | — | 2.9 GB | ~379 | same size |
Specifications
- Publisher
- XHToken
- Parameters
- 1.7B
- Architecture
- Spark2_5ForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-08-24
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Spark X2.5 1.7B need?
Spark X2.5 1.7B requires 1.4 GB of VRAM at Q4_K_M, or 3.8 GB at BF16. Full 1049K context adds up to 60.0 GB (61.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 4.8 bits ÷ 8 = 1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 60.5 GB (at full 1049K context)
VRAM usage by quantization
What's the best quantization for Spark X2.5 1.7B?
For Spark X2.5 1.7B, Q4_K_M (1.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Spark X2.5 1.7B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Spark X2.5 1.7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Spark X2.5 1.7B locally?
Yes — Spark X2.5 1.7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Spark X2.5 1.7B?
At Q4_K_M, Spark X2.5 1.7B can reach ~3333 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~455 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.44 × 0.65 = ~3611 tok/s
Estimated speed at Q4_K_M (1.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Spark X2.5 1.7B?
At Q4_K_M, the download is about 1.02 GB. The full-precision BF16 version is 3.42 GB. The smallest option (Q2_K) is 0.73 GB.
Which GPUs can run Spark X2.5 1.7B?
52 consumer GPUs can run Spark X2.5 1.7B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Spark X2.5 1.7B?
163 devices with unified memory can run Spark X2.5 1.7B at Q4_K_M (1.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.