hmellor/tiny-random-LlamaForCausalLM
hmellor/tiny-random-LlamaForCausalLM
Parameters
1.1M
Context
8K
Downloads
4.2M
Likes
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Architecture
llama
2 layers · 4 KV heads · head 64 · hidden 16
GPU Requirements
VRAM / RAM / disk estimates by quantization and usage scenario
| Quantization | Minimum | Recommended | Production |
|---|---|---|---|
| FP1616bit | 2.2 GB RAM 32.0 · Disk 39.0 | 2.5 GB RAM 32.0 · Disk 65.0 | 3.6 GB RAM 32.0 · Disk 104.0 |
| FP88bit | 2.2 GB RAM 32.0 · Disk 39.0 | 2.5 GB RAM 32.0 · Disk 65.0 | 3.6 GB RAM 32.0 · Disk 104.0 |
| INT88bit | 2.2 GB RAM 32.0 · Disk 39.0 | 2.5 GB RAM 32.0 · Disk 65.0 | 3.6 GB RAM 32.0 · Disk 104.0 |
| AWQ4bit | 2.2 GB RAM 32.0 · Disk 39.0 | 2.5 GB RAM 32.0 · Disk 65.0 | 3.6 GB RAM 32.0 · Disk 104.0 |
| GPTQ4bit | 2.2 GB RAM 32.0 · Disk 39.0 | 2.5 GB RAM 32.0 · Disk 65.0 | 3.6 GB RAM 32.0 · Disk 104.0 |
| GGUF4bit | 2.2 GB RAM 32.0 · Disk 39.0 | 2.5 GB RAM 32.0 · Disk 65.0 | 3.6 GB RAM 32.0 · Disk 104.0 |
GPU Recommendations
1× RTX 3080 Ti
12 GB VRAM
≈ 0 tok/s (N/A — benchmark unavailable)
Effective production throughput ≈ 0 tok/s
score 0.5493
1× RTX 3080
10 GB VRAM
≈ 0 tok/s (N/A — benchmark unavailable)
Effective production throughput ≈ 0 tok/s
score 0.5595
8× H200 SXM
1128 GB VRAM
≈ 0 tok/s (N/A — benchmark unavailable)
Effective production throughput ≈ 0 tok/s
score 0.2197
1× RTX 3090
24 GB VRAM
≈ 0 tok/s (N/A — benchmark unavailable)
Effective production throughput ≈ 0 tok/s
score 0.4948
💬 Community — real deployment experience
0 Articles · 0 Benchmarks
GPUs that fit (single card)
RTX 3080
10 GB VRAM
$58.6
/mo · vast
Tesla V100
16 GB VRAM
$66.4
/mo · vast
RTX 4060 Ti
8 GB VRAM
$78.4
/mo · vast
RTX 3080 Ti
12 GB VRAM
$79.5
/mo · vast
RTX 3090
24 GB VRAM
$88.4
/mo · vast