DeepSeek R1 Distill 8B is emerging as the speed champion for local AI inference on budget hardware, delivering token latency roughly half that of comparable Llama models while maintaining reasoning capabilities. New benchmarks from Puget Systems reveal that the model generates output tokens in just 15 milliseconds on Intel's Arc Pro B70 GPU, compared to 28 milliseconds for Llama 3.1 8B, despite both being built on the same underlying Llama-8B architecture.

The finding matters because it suggests that DeepSeek's distillation approach, which compresses reasoning models into smaller, faster versions, may offer a practical path for developers building local AI applications without cloud API costs. A single Arc Pro B70 card costs $949 and includes 32 gigabytes of video memory, making it roughly half the price per card of NVIDIA's RTX 5090 while offering twice the aggregate memory when four cards are combined.

How Can Developers Set Up Multi-GPU Local Inference?

• Single-Card Setup: A single Arc Pro B70 comfortably runs 8-billion-parameter models like DeepSeek R1 Distill 8B and Llama 3.1 8B, delivering interactive response speeds suitable for chat applications with 66.9 tokens per second throughput.
• Four-Card Configuration: Four Arc Pro B70 cards installed in a workstation provide 128 gigabytes of combined video memory, enabling larger 27-billion to 35-billion parameter models that represent the current sweet spot for serious local inference work.
• Cost Comparison: A four-card setup totals approximately $3,800 in GPU hardware costs and delivers twice the aggregate memory of two RTX 5090s priced around $4,000, though NVIDIA cards offer faster per-GPU memory bandwidth.

Why Is DeepSeek R1 Distill Faster Than Llama on This Hardware?

The performance gap appears consistent across different GPU vendors. Puget Systems observed the same pattern on AMD's R9700 GPU, where DeepSeek R1 Distill achieved 16 milliseconds inter-token latency compared to 31 milliseconds for Llama 3.1 8B, suggesting the advantage stems from the model's architecture rather than hardware-specific optimization.

DeepSeek R1 Distill 8B achieved 66.9 tokens per second throughput at single-user concurrency, scaling to 486 tokens per second when eight simultaneous users submitted requests, a 7.3-fold improvement that demonstrates near-linear scaling on a single GPU. The model's 15-millisecond inter-token latency means token delivery remains exceptionally smooth even under load, a critical factor for interactive applications where users perceive delays above 50 milliseconds.

The reasoning-distilled nature of DeepSeek R1 means it generates internal chain-of-thought tokens before producing visible output, which is reflected in higher per-request latency overall. However, the model remains fully suitable for interactive use when measured with the correct extended measurement window to account for this reasoning phase.

What Are the Practical Limits of Current Hardware?

The Arc Pro B70 setup reveals clear boundaries for what models fit on local hardware. Models up to 8 billion parameters run comfortably on a single card, while the 27-billion to 35-billion parameter tier requires multi-GPU tensor parallelism across all four cards to fit in memory.

Larger models like DeepSeek V4 FlashMoE, which has 284 billion parameters with 13 billion active, cannot run on this hardware even with four cards combined. The 128-gigabyte aggregate memory ceiling means developers must choose between running smaller models locally or accepting cloud API latency and costs for frontier-scale models.

One software limitation affects model selection: Intel's XPU backend currently supports only unquantized FP16 weights and lacks the AWQ and GPTQ dequantization kernels available on NVIDIA's CUDA platform. However, Intel's LLM Scaler container adds INT4 and FP8 online quantization as well as GPTQ support, expanding the practical model roster beyond what raw memory suggests.

What Does This Mean for the Economics of Local AI?

The cost-per-token calculation shifts dramatically when comparing local inference to cloud APIs. A four-card Arc Pro B70 setup draws approximately 920 watts of power under full load, translating to roughly $0.11 per hour in electricity costs at typical US rates. For organizations running continuous inference workloads, this hardware investment breaks even within months compared to cloud API pricing.

The Qwen2.5 3-billion parameter model achieved 72.9 tokens per second on a single card, generating roughly 3,300 words per minute with a 48-millisecond time-to-first-token that feels nearly instantaneous to users. This performance level makes local deployment viable for customer-facing chatbots, content moderation, and code completion tools where latency sensitivity is high.

Developers planning local inference deployments should account for the software setup complexity. Getting multi-GPU inference running on Intel's Battlemage architecture requires specific container configuration, environment variable tuning, driver conflict resolution, and PCIe topology configuration that differs from NVIDIA's mature CUDA ecosystem.