Laptop that can run 20B parameter model inferencing. under AU$3000

Key Points

- It seems likely that finding a laptop under AU$3000 capable of running a 20B parameter model for inferencing is challenging due to memory constraints.

- Research suggests that such models typically require at least 15 GB of GPU memory, but most laptops in this price range have GPUs with 8 GB, like the RTX 4070.

- The evidence leans toward recommending high-end laptops like the MSI Katana 15 with RTX 4070 (8 GB memory) for AU$2,499, with optimizations for smaller sequence lengths.

Laptop Recommendation

Given the budget of under AU$3000, the MSI Katana 15 with an RTX 4070 GPU (8 GB memory) is a top choice. It features:

- CPU: Intel Core i7-13700H

- RAM: 16 GB DDR5

- Storage: 512 GB SSD

- Display: 15.6″ FHD 144 Hz

- Price: Approximately AU$2,499 (available at Scorptec Computers).

This laptop may not fully meet the memory needs for a 20B parameter model without compromises, but it’s the best option within the budget.

Optimizations and Limitations

Running a 20B parameter model on an 8 GB GPU requires optimizations like 4-bit quantization (reducing memory to ~10 GB for weights) and limiting sequence length to reduce KV cache memory (e.g., ~3 GB for 2048 tokens). Users may need to accept slower performance or shorter context windows.

Unexpected Detail

An unexpected finding is that while Nvidia GPUs are preferred for large language models due to CUDA optimization, AMD GPUs like the Radeon RX 7900M (16 GB memory) exist but are found in laptops priced above AU$4000, exceeding the budget.

Survey Note: Detailed Analysis of Laptops for 20B Parameter Model Inferencing Under AU$3000

This section provides a comprehensive analysis of the feasibility and options for finding a laptop under AU$3000 capable of running a 20B parameter model for inferencing, based on current market data and technical requirements as of February 28, 2025.

Model Requirements and Memory Estimation

To run a 20B parameter model for inferencing, significant computational resources are needed, particularly GPU memory. The analysis begins by estimating memory requirements:

- Model Weights: Assuming 4-bit quantization, each parameter requires 0.5 bytes (4 bits). For 20 billion parameters, this translates to 20B * 0.5 bytes = 10 GB of memory for the weights alone. This is a common optimization technique, as evidenced by research on quantization of large language models to 4-bit.

- KV Cache: The Key-Value cache for transformer models depends on sequence length. For a sequence length of 2048, using Llama 2 7B as a reference (32 layers, 32 heads, head dimension 128), the KV cache memory per layer per token is approximately 16,384 bytes. Scaling for a 20B model with potentially 99 layers (based on parameter scaling), the total KV cache for 2048 tokens is estimated at ~3 GB, derived from (99/32) * 1 GB (from Llama 2 7B calculations).

- Activations and Overheads: Additional memory for activations and buffers is estimated at ~2 GB, based on general guidelines from GPU memory requirements for LLMs. This brings the total estimated memory requirement to ~15 GB.

GPU Market Analysis Under AU$3000

Laptops under AU$3000 typically feature GPUs with 8 GB to 12 GB of memory, which falls short of the 15 GB requirement. The analysis focused on Nvidia GPUs due to their CUDA optimization for large language models, though AMD options were considered.

- Nvidia RTX Series:
  - - RTX 4090 (16 GB memory): Found in laptops priced around AU$3500-AU$4000, exceeding the budget (e.g., MSI Raider A18 HX A7VIG at AU$3,999).
  - - RTX 4080 (12 GB memory): Typically priced above AU$3000, with examples like MSI Vector 16 HX at AU$3,500, also over budget.
  - - RTX 4070 (8 GB memory): Available under AU$3000, with models like MSI Katana 15 at AU$2,499 and ASUS TUF F15 at AU$2,599, fitting the budget but with insufficient memory.

- AMD GPUs: The AMD Radeon RX 7900M (16 GB memory) was identified as a potential alternative, but laptops like the Alienware m18 R1 with this GPU are priced around AU$4,200, far exceeding the budget (AMD Radeon RX 7900M in Alienware m18).

Laptop Options and Specifications

Given the budget constraint, the focus shifted to laptops with the most powerful GPUs under AU$3000. A table summarizing the top options is provided below:

Model	GPU	GPU Memory	CPU	RAM	Storage	Display	Price (AU$)
MSI Katana 15	RTX 4070	8 GB	Intel Core i7-13700H	16 GB	512 GB SSD	15.6″ FHD 144 Hz	~2,499
ASUS TUF F15	RTX 4070	8 GB	Intel Core i7-13700H	16 GB	512 GB SSD	15.6″ FHD 144 Hz	~2,599

These laptops, while within budget, have only 8 GB of GPU memory, which is insufficient for the estimated 15 GB requirement without optimizations.

Optimizations and Compromises

To make the 20B model run on these laptops, several optimizations are necessary:

- Quantization: As mentioned, 4-bit quantization reduces weight memory to 10 GB, but peak memory usage during inference (including dequantized layers) may still exceed 8 GB. Libraries like bitsandbytes (4-bit quantization with bitsandbytes) support this, with dequantized layer memory estimated at ~0.4 GB per layer, adding to the compressed 10 GB.

- Sequence Length Reduction: Reducing the sequence length (e.g., to 1024) can halve KV cache memory (~1.5 GB), making total memory usage ~13.9 GB, still above 8 GB but closer. This is supported by LLM inference guide.

- System RAM Utilization: Some libraries allow swapping parts of the model to system RAM, but this significantly impacts performance due to slower access times compared to GPU memory.

Users may need to accept slower inference speeds or limited context windows, which could affect applications requiring long conversations or large context.

Alternative Considerations

- External GPU Enclosures: Laptops with Thunderbolt 4 ports (e.g., Lenovo ThinkPad X1 Carbon Gen 11) could use external GPUs, but this adds cost and complexity, potentially exceeding the budget.

- Used or Refurbished Laptops: While not explored in detail, users might find older models with higher memory GPUs (e.g., RTX 3080 with 16 GB) at lower prices, but reliability is a concern. For example, refurbished ASUS ROG Strix SCAR 17 starts at higher prices.

- Desktop PCs: While outside the scope (as the query specifies laptops), desktops under AU$3000 with higher-end GPUs (e.g., RTX 4080) could be considered for better performance, but portability is lost.

Conclusion

The analysis suggests that no laptop under AU$3000 has sufficient GPU memory (at least 12-16 GB) to comfortably run a 20B parameter model for inferencing with a sequence length of 2048 without significant compromises. The recommended options, like the MSI Katana 15 with RTX 4070, require optimizations such as reduced sequence lengths and quantization, potentially impacting performance. Users should be prepared for these trade-offs or consider stretching their budget for higher-end models.