RTX 5060 Ti 16GB for Telegram Bot Backend

Telegram has the highest ceiling for bot abuse: 30 messages/sec to different chats and 1 message/sec to the same chat. A self-hosted LLM on the RTX 5060 Ti 16GB at our UK dedicated GPU hosting keeps pace without per-token fees. The Blackwell card runs Mistral 7B FP8 at 122 t/s and Llama 3 8B FP8 at 112 t/s, which matches or beats Telegram’s delivery ceiling on one card.

Contents

• python-telegram-bot setup
• Webhook vs long-polling
• Streaming responses
• Capacity by bot size
• Image and voice commands

Bot setup

Use python-telegram-bot v20+. The handler calls your vLLM endpoint and edits the reply as tokens arrive, giving users a typing-effect response.

async def handle(update, context):
    msg = await update.message.reply_text("...")
    buf = ""
    async for chunk in llm.chat.completions.create(
        model="mistral-7b-fp8",
        messages=[{"role":"user","content":update.message.text}],
        stream=True,
    ):
        buf += chunk.choices[0].delta.content or ""
        if len(buf) % 60 == 0:
            await msg.edit_text(buf)
    await msg.edit_text(buf)

Webhook vs long-polling

Run webhooks behind Nginx with a Cloudflare origin certificate. Telegram accepts any CA, so there is no Let’s Encrypt dance. For low-volume bots, long-polling is fine and simpler.

Streaming

Telegram allows roughly 1 edit/sec on the same message. Buffer tokens and edit every 60-80 characters to stay under the rate limit while keeping the typing effect. The overall perceived latency for a 300-token reply drops from 2.5 s blob-delivery to 300 ms first-word-visible.

Capacity

Image and voice commands

/image {prompt} routes to SDXL Lightning 4-step at about 2.2 seconds for 1024×1024 on the same card, which you send back as a JPEG with reply_photo. Telegram voice messages (Opus OGG) can be transcribed with Whisper Turbo at roughly 60x real-time on the 5060 Ti, so a 30-second voice note is transcribed in under a second. See our Whisper benchmark.

See also: chatbot backend, customer support, Whisper benchmark, FP8 Llama deployment, internal tooling.