As of mid‑2026, Kneron has not publicly disclosed a firm, single-number “maximum token count” for the KNEO 350 in the same way that software APIs (like “200K tokens”) do. Instead, the effective number of tokens you can handle depends on:

  • The LLM/model you run (architecture, parameter size, quantization)
  • How you configure context length and batch size
  • Memory available on the KNEO 350 and your system (RAM, SRAM, etc.)
  • Whether you’re doing single-request inference or streaming / multi-turn sessions

Kneron’s recent announcements focus on KNEO 350 as an enterprise on‑premises AI substrate that can run large models locally (e.g., agentic AI stacks, OpenClaw, and large LLMs), but they describe capability in terms of model size and performance rather than a fixed “token limit”.

What Kneron actually says about KNEO 350 capacity

Kneron’s messaging for KNEO 350 / KNEO Rack clusters emphasizes:

  • Running large language models and agentic AI frameworks locally and securely
  • Data-center-class performance without traditional GPU-server energy costs
  • Support for enterprise workflows that keep proprietary knowledge and business data on-prem

They do not publish a hard “X tokens max” number. In practice, for many edge/on-prem AI platforms, the “token ceiling” is effectively:

Whatever context length your chosen model supports, limited by available memory and compute.

For example, if you run a 7B–13B quantized LLM with a 32K or 64K context window, the KNEO 350 can in principle support that, as long as:

  • You have enough RAM to hold the model + context
  • Your inference stack (e.g., OpenClaw, custom runtime) is configured for that context length

How to think about “how many tokens” in practice

If you’re trying to estimate real-world limits:

  1. Pick your model and context length
    • Example: a 13B model with 32K token context → ~32K tokens per request.
    • Some models support 64K, 128K, or even more; you can choose based on your needs.
  2. Check memory requirements
    • Quantized models (e.g., 4-bit) drastically reduce memory.
    • The total memory needed ≈ model weights + KV cache for the context.
    • For many edge/on-prem SKUs, 32K–64K context is common; 128K may require more RAM.
  3. Confirm with Kneron or your runtime
    • Ask Kneron support or your deployment team:
      • “What max context length do you recommend for KNEO 350 with [model X]?”
      • “What RAM / buffer sizes are needed for 32K / 64K tokens?”
    • This is where you’ll get an actual, repeatable number for your setup.

Bottom line

  • There is no officially published single token limit like “KNEO 350 supports up to N tokens” in Kneron’s public materials as of July 2026.
  • Practically, the KNEO 350 can support whatever token context length your chosen LLM and configuration allow , constrained by memory and compute.
  • Typical enterprise deployments on similar hardware often target 32K–64K tokens per request, with higher values possible if you have sufficient RAM and tune the inference stack.

If you tell me:

  • Which model(s) you want to run (e.g., Llama 3 8B, Mixtral 7B, etc.)
  • Whether you need single-shot or multi-turn context

I can sketch a more concrete estimate of realistic token ranges for KNEO 350 based on common memory and performance patterns. Information gathered from public forums or data available on the internet and portrayed here.