Salamandra 7B Instruct — Hardware Requirements & GPU Compatibility
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Salamandra 7B Instruct is a 7.8-billion-parameter multilingual model developed by the Barcelona Supercomputing Center (BSC-LT) as part of a European initiative to build high-quality open language models. It has particular strength in Iberian languages including Spanish, Catalan, Portuguese, and Basque, while also supporting English and other major European languages. This model is an excellent choice for users who need strong performance in Spanish or other Iberian languages that are often underserved by mainstream LLMs. Running it locally ensures data privacy for sensitive multilingual workflows, and at 7B parameters it fits comfortably on a single consumer GPU with 8 GB or more of VRAM.
70.3K downloads
76 likesSpecifications
- Publisher
- BSC-LT
- Parameters
- 7.8B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 256,000
- Release Date
- 2024-09-30
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Salamandra 7B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.9 GB | 4.7 GB | 3.30 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.0 GB | 4.8 GB | 3.40 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.4 GB | 5.2 GB | 3.79 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.5 GB | 5.3 GB | 3.88 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.2 GB | 6.0 GB | 4.66 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.1 GB | 6.9 GB | 5.53 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.0 GB | 7.8 GB | 6.41 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.3 GB | 9.1 GB | 7.77 GB | 8-bit quantization, near-lossless |
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 Salamandra 7B Instruct?
Q4_K_M · 5.2 GBSalamandra 7B Instruct (Q4_K_M) requires 5.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 8K context window can add up to 0.8 GB, bringing total usage to 6.0 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Salamandra 7B Instruct?
Q4_K_M · 5.2 GB58 devices with unified memory can run Salamandra 7B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Salamandra 7B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Salamandra 7B Instruct need?
Salamandra 7B Instruct requires 5.2 GB of VRAM at Q4_K_M, or 16.1 GB at BF16. Full 8K context adds up to 0.8 GB (6.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.8B × 4.8 bits ÷ 8 = 4.7 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.3 GB (at full 8K context)
VRAM usage by quantization
Q4_K_M5.2 GBQ4_K_M + full context6.0 GB- What's the best quantization for Salamandra 7B Instruct?
For Salamandra 7B Instruct, Q4_K_M (5.2 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.4 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 3.8 GB.
VRAM requirement by quantization
IQ3_XS3.8 GBQ3_K_M4.4 GBQ4_14.9 GBQ4_K_M ★5.2 GBQ5_15.9 GBBF1616.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Salamandra 7B Instruct on a Mac?
Salamandra 7B Instruct requires at least 3.8 GB at IQ3_XS, 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 Salamandra 7B Instruct locally?
Yes — Salamandra 7B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 5.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Salamandra 7B Instruct?
At Q4_K_M, Salamandra 7B Instruct can reach ~841 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~125 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 ÷ 5.2 × 0.65 = ~994 tok/s
Estimated speed at Q4_K_M (5.2 GB)
~994 tok/s~125 tok/s~994 tok/s~841 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Salamandra 7B Instruct?
At Q4_K_M, the download is about 4.66 GB. The full-precision BF16 version is 15.54 GB. The smallest option (IQ3_XS) is 3.20 GB.
- Which GPUs can run Salamandra 7B Instruct?
50 consumer GPUs can run Salamandra 7B Instruct at Q4_K_M (5.2 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 Salamandra 7B Instruct?
59 devices with unified memory can run Salamandra 7B Instruct at Q4_K_M (5.2 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.