Local LLM hardware cost efficiency

Local Large Language Model (LLM) hardware efficiency is primarily determined by the relationship between memory bandwidth and the total cost of the system Verified Answer #3. For single-user workloads, the token generation stage is memory-bandwidth bound rather than compute-bound Verified Answer #2. This means that throughput scales linearly with the hardware's effective memory bandwidth, provided the model weights fit entirely within the available VRAM or unified memory Verified Answer #3.

Determinants of Efficiency

The efficiency of a hardware configuration, measured in tokens-per-second per dollar (TPS/$), is influenced by several critical factors Verified Answer #1:

• Memory Bandwidth: Because the processor must retrieve the entire model's weights to generate each individual token, higher bandwidth directly increases generation speed Verified Answer #2.
• VRAM Capacity: The model must fit within the available memory with enough headroom for the KV cache and system overhead to avoid performance degradation from offloading to system RAM Verified Answer #2Verified Answer #1.
• Software Support: Mature software backends like CUDA, ROCm, or Metal are necessary to translate theoretical hardware performance into practical throughput Verified Answer #1.

8B Parameter Model Efficiency

For 8B-class models quantized to 4-bit precision, the hardware requirement is approximately 4–5 GB of VRAM Verified Answer #2. A single mid-range GPU with 12 GB to 16 GB of VRAM is considered the optimal configuration for these models Verified Answer #2Verified Answer #1.

• Value Leader (New): The Intel Arc B580 12GB is the strongest new-hardware value pick, offering approximately 45–55 TPS at a setup cost of roughly $180 to $249 Verified Answer #3Verified Answer #2.
• Value Leader (Used): Used RTX 3060 12GB or RTX 2080 Ti builds are highly competitive on a pure cost basis Verified Answer #1.
• Performance Leader: The RTX 5060 Ti 16GB provides higher throughput of 80–110 TPS but at a higher cost of approximately $450, resulting in a lower TPS/$ rating than the Intel Arc B580 Verified Answer #3.

70B Parameter Model Efficiency

Running 70B models at 4-bit quantization requires significantly more memory, typically between 40 GB and 45 GB of VRAM Verified Answer #3. This creates a "VRAM capacity cliff" where users must invest in multi-GPU setups or premium high-memory systems Verified Answer #2.

• Consumer Value Winner: A dual-RTX 3090 24GB build using used hardware is the most efficient consumer-grade configuration, providing 48 GB of total VRAM and high aggregate bandwidth Verified Answer #1.
• New Hardware Alternative: For users requiring new hardware, a dual RX 7900 XTX 24GB setup is the primary mainstream alternative, though it may face more software friction than NVIDIA-based systems Verified Answer #1.
• Unified Memory Alternative: The Mac Studio M4 Max offers a high-capacity unified memory solution but at a significantly higher cost-per-token compared to used GPU builds Verified Answer #3.