RedPajama INCITE 7B Base — Hardware Requirements & GPU Compatibility
Chat
RedPajama-INCITE-7B-Base is Together Computer's open base pretrained language model, not instruction-tuned, with roughly 6.9 billion parameters trained on the RedPajama-Data-1T dataset, an open reproduction of the corpus used to train Meta's original LLaMA. It was developed with a consortium including Ontocord.ai, ETH DS3Lab, Stanford CRFM and Hazy Research, and LAION, using compute awarded through the 2023 INCITE program. Instruction-tuned and chat variants, RedPajama-INCITE-7B-Instruct and RedPajama-INCITE-7B-Chat, were released alongside it. At under 7 billion parameters it runs on a single consumer GPU. Context length is 2,048 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. It was published in May 2023, as one of the first fully open, commercially usable base models trained on openly licensed data.
988 downloads
92 likesSpecifications
- Publisher
- togethercomputer
- Parameters
- 7B
- Architecture
- GPTNeoXForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,432
- Release Date
- 2023-05-04
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does RedPajama INCITE 7B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 3.3 GB | — | 2.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 3.8 GB | — | 3.41 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 4.6 GB | — | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 5.5 GB | — | 4.99 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 6.3 GB | — | 5.78 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 7.7 GB | — | 7.00 GB | 8-bit quantization, near-lossless |
| FP16est. | 16.00 | 15.4 GB | — | 14.00 GB | Full half-precision — baseline for inference |
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 RedPajama INCITE 7B Base?
Q4_K_M · 4.6 GBRedPajama INCITE 7B Base (Q4_K_M) requires 4.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run RedPajama INCITE 7B Base?
Q4_K_M · 4.6 GB59 devices with unified memory can run RedPajama INCITE 7B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does RedPajama INCITE 7B Base need?
RedPajama INCITE 7B Base requires 4.6 GB of VRAM at Q4_K_M, or 15.4 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.6 GB- What's the best quantization for RedPajama INCITE 7B Base?
For RedPajama INCITE 7B Base, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (5.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.3 GB.
VRAM requirement by quantization
Q2_K3.3 GBQ4_K_M ★4.6 GBQ5_K_M5.5 GBQ6_K6.3 GBQ8_07.7 GBFP1615.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run RedPajama INCITE 7B Base on a Mac?
RedPajama INCITE 7B Base requires at least 3.3 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 RedPajama INCITE 7B Base locally?
Yes — RedPajama INCITE 7B Base can run locally on consumer hardware. At Q4_K_M quantization it needs 4.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is RedPajama INCITE 7B Base?
At Q4_K_M, RedPajama INCITE 7B Base can reach ~1039 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~142 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 ÷ 4.6 × 0.65 = ~1126 tok/s
Estimated speed at Q4_K_M (4.6 GB)
~1126 tok/s~142 tok/s~1126 tok/s~1039 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of RedPajama INCITE 7B Base?
At Q4_K_M, the download is about 4.20 GB. The full-precision FP16 version is 14.00 GB. The smallest option (Q2_K) is 2.98 GB.
- Which GPUs can run RedPajama INCITE 7B Base?
52 consumer GPUs can run RedPajama INCITE 7B Base at Q4_K_M (4.6 GB). 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 RedPajama INCITE 7B Base?
59 devices with unified memory can run RedPajama INCITE 7B Base at Q4_K_M (4.6 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.