Meshllm FP16 17B Parameters

VRAM Requirements for
Llama-4-Maverick-17B-128E-Instruct-UD-Q4_K_XL-layers FP16

To run Llama-4-Maverick-17B-128E-Instruct-UD-Q4_K_XL-layers locally at FP16 quantization, you need at minimum 36.28 GB of GPU VRAM.

36.28 GB
Required VRAM
34 GB
File Size
8K tokens
Context Window
17B
Parameters
Estimated VRAM Required
36.28
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (FP16) 34 GB
Context Overhead (8,192 tokens × 17B × 2 ÷ 1M) 0.279 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 36.28 GB

Try a Different Quantization

Use the interactive calculator to compare Llama-4-Maverick-17B-128E-Instruct-UD-Q4_K_XL-layers across all available formats.

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Llama-4-Maverick-17B-128E-Instruct-UD-Q4_K_XL-layers — Other Quantizations

FP16 Current
36.28 GB
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Frequently Asked Questions

Can I run Llama-4-Maverick-17B-128E-Instruct-UD-Q4_K_XL-layers FP16 on a consumer GPU?
Running Llama-4-Maverick-17B-128E-Instruct-UD-Q4_K_XL-layers FP16 locally requires 36.28 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run Llama-4-Maverick-17B-128E-Instruct-UD-Q4_K_XL-layers?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is FP16 quality good enough for production?
FP16/BF16 is the standard precision used for production inference and serves as the quality baseline. All fine-tuned models are typically served at this precision.