Tagalog Seallm 7B V1 — Hardware Requirements & GPU Compatibility
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Tagalog Seallm 7B V1 is a 7.4B-parameter open language model from 922-Narra. It supports a context window of up to 32,768 tokens. At FP16 it needs about 15.32 GB of VRAM — see which GPUs and Macs can run it below.
12 downloads
1 likesSpecifications
- Publisher
- 922-Narra
- Parameters
- 7.4B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 48,384
- Release Date
- 2025-01-31
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Tagalog Seallm 7B V1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 15.3 GB | 19.4 GB | 14.75 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Tagalog Seallm 7B V1?
FP16 · 15.3 GBTagalog Seallm 7B V1 (FP16) requires 15.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. Using the full 33K context window can add up to 4.0 GB, bringing total usage to 19.4 GB. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Tagalog Seallm 7B V1?
FP16 · 15.3 GB27 devices with unified memory can run Tagalog Seallm 7B V1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Tagalog Seallm 7B V1 need?
Tagalog Seallm 7B V1 requires 15.3 GB of VRAM at FP16. Full 33K context adds up to 4.0 GB (19.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.4B × 16 bits ÷ 8 = 14.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.6 GB (at full 33K context)
VRAM usage by quantization
FP1615.3 GBFP16 + full context19.4 GB- Can I run Tagalog Seallm 7B V1 on a Mac?
Tagalog Seallm 7B V1 requires at least 15.3 GB at FP16, 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 Tagalog Seallm 7B V1 locally?
Yes — Tagalog Seallm 7B V1 can run locally on consumer hardware. At FP16 quantization it needs 15.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Tagalog Seallm 7B V1?
At FP16, Tagalog Seallm 7B V1 can reach ~190 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~43 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 15.3 × 0.55 = ~190 tok/s
Estimated speed at FP16 (15.3 GB)
~190 tok/s~43 tok/s~142 tok/s~118 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Tagalog Seallm 7B V1?
At FP16, the download is about 14.75 GB.
- Which GPUs can run Tagalog Seallm 7B V1?
17 consumer GPUs can run Tagalog Seallm 7B V1 at FP16 (15.3 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Tagalog Seallm 7B V1?
27 devices with unified memory can run Tagalog Seallm 7B V1 at FP16 (15.3 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.