Spreadsheet RL 4B — Hardware Requirements & GPU Compatibility
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Spreadsheet RL 4B is a 4.4B-parameter open language model from Spreadsheet-RL. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 3.14 GB of VRAM — see which GPUs and Macs can run it below.
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- Publisher
- Spreadsheet-RL
- Parameters
- 4.4B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-05-23
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Spreadsheet RL 4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 2.4 GB | 26.3 GB | 1.87 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 2.6 GB | 26.6 GB | 2.15 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 3.1 GB | 27.1 GB | 2.65 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 3.6 GB | 27.6 GB | 3.14 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 4.1 GB | 28.1 GB | 3.64 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 4.9 GB | 28.9 GB | 4.41 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 9.3 GB | 33.3 GB | 8.82 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Spreadsheet RL 4B?
Q4_K_M · 3.1 GBSpreadsheet RL 4B (Q4_K_M) requires 3.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 262K context window can add up to 24.0 GB, bringing total usage to 27.1 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Spreadsheet RL 4B?
Q4_K_M · 3.1 GB59 devices with unified memory can run Spreadsheet RL 4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Spreadsheet RL 4B need?
Spreadsheet RL 4B requires 3.1 GB of VRAM at Q4_K_M, or 9.3 GB at BF16. Full 262K context adds up to 24.0 GB (27.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.4B × 4.8 bits ÷ 8 = 2.6 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 24.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M3.1 GBQ4_K_M + full context27.1 GB- What's the best quantization for Spreadsheet RL 4B?
For Spreadsheet RL 4B, Q4_K_M (3.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.4 GB.
VRAM requirement by quantization
Q2_K2.4 GBQ4_K_M ★3.1 GBQ5_K_M3.6 GBQ6_K4.1 GBQ8_04.9 GBBF169.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Spreadsheet RL 4B on a Mac?
Spreadsheet RL 4B requires at least 2.4 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Spreadsheet RL 4B locally?
Yes — Spreadsheet RL 4B can run locally on consumer hardware. At Q4_K_M quantization it needs 3.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Spreadsheet RL 4B?
At Q4_K_M, Spreadsheet RL 4B can reach ~1401 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~209 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 ÷ 3.1 × 0.65 = ~1656 tok/s
Estimated speed at Q4_K_M (3.1 GB)
~1656 tok/s~209 tok/s~1656 tok/s~1401 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Spreadsheet RL 4B?
At Q4_K_M, the download is about 2.65 GB. The full-precision BF16 version is 8.82 GB. The smallest option (Q2_K) is 1.87 GB.
- Which GPUs can run Spreadsheet RL 4B?
50 consumer GPUs can run Spreadsheet RL 4B at Q4_K_M (3.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Spreadsheet RL 4B?
59 devices with unified memory can run Spreadsheet RL 4B at Q4_K_M (3.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.