I am experiencing an unusual latency issue with the DeepSeek V3.2 API.
The total wall-clock time for a simple request is approximately 5.5 seconds, but the total_duration (or equivalent performance metric) returned in the API response indicates only 0.56 seconds. This suggest a significant overhead of nearly 5 seconds occurring somewhere between the gateway and the model inference.
curl:
time curl -H “Authorization: Bearer apikey” -H “Content-Type: application/json” -d ‘{
“stream”: false,
“model”: “DeepSeek-V3.2”,
“messages”: [
{
“role”: “system”,
“content”: “You are a helpful assistant”
},
{
“role”: “user”,
“content”: “Hello!”
}
]
}’ -X POST https://api.sambanova.ai/v1/chat/completions
Hi @zhouze1008
Thanks for reporting this. We’ve replicated the issue on our side and are currently investigating it further. We’ll get back to you with updates as soon as we have more information.
Thanks,
Tanish S.
1 Like
Hi @zhouze1008 please find below observation from my side on your queries
Observation:
Model inference itself is typically very fast (often achieving high tokens/second on platforms like SambaNova with models such as DeepSeek-V3.2). However, the total end-to-end latency can vary significantly due to waiting time in a shared scheduling queue.
This is normal behavior on multi-tenant, shared-capacity systems:
-
Resources are dynamically allocated.
-
When demand is high or after idle periods, incoming requests may queue briefly (adding 1–several seconds).
-
Cold starts or resource reallocation after inactivity are the primary causes of spikes.
These delays are not a reflection of model quality or raw inference performance — they are infrastructure scheduling artifacts.
Recommended Optimizations to Improve Latency & Responsiveness
Implement these strategies (in order of impact/ease) to minimize perceived and actual latency without affecting output quality.
-
Enable Streaming Responses (Highest Impact for Perceived Latency)
Streaming is the single most effective way to improve user-perceived responsiveness.
-
Tokens start flowing back almost immediately after inference begins → Time-to-first-token (TTFT) becomes very low.
-
Users see progress right away instead of waiting for the full response.
-
Especially valuable for longer generations or chat/agentic flows.
How to enable (OpenAI-compatible API):
JSON
{
"model": "DeepSeek-V3.2",
"messages": [...],
"stream": true
}
Client handling tip: Iterate over the SSE stream and append tokens as they arrive (most SDKs like openai-python handle this natively).
-
Batch Small / Similar Prompts (Throughput & Queue Efficiency)
On shared systems, individual small requests compete for scheduling slots → higher chance of queuing.
Batching reduces the number of separate queue entries and amortizes scheduling overhead.
When to use:
-
Multiple independent short queries from the same user/session (e.g., multi-turn chat preprocessing, parallel tool calls, or background tasks).
-
Throughput-oriented workloads (non-real-time).
Implementation approaches:
Benefit: Fewer queue entries → lower average wait time + better overall system utilization.
-
Implement Keep-Alive / Warm-Up Strategy (Reduces Cold-Start Variability)
In shared environments, idle periods (> ~30–60 seconds) can trigger resource reallocation or down-scaling → next request incurs “warm-up” delay (queue + allocation).
Verification test — Send two near-identical requests:
I’m giving above implementation in below python script:
Python
#!/usr/bin/env python3
"""
SambaNova / OpenAI-compatible API Latency Optimization Demo
------------------------------------------------------------
Combines three main strategies in one file:
1. Streaming responses → best perceived latency
2. Batching small prompts → reduces queue contention
3. Keep-alive pings → minimizes cold-start delays
Features:
- Configurable keep-alive in background thread
- Simple batching example
- Streaming + non-streaming modes
- Basic timing and statistics
Usage:
python latency_optimizations.py
You need to set your real API_KEY below.
"""
import os
import time
import json
import threading
import queue
from typing import List, Dict, Any
import requests
from concurrent.futures import ThreadPoolExecutor
# ────────────────────────────────────────────────
# CONFIGURATION
# ────────────────────────────────────────────────
API_URL = "https://api.sambanova.ai/v1/chat/completions"
API_KEY = os.getenv("SAMBANOVA_API_KEY") or "your-api-key"
MODEL = "DeepSeek-V3.2" # or Llama-3.1-70B, etc.
HEADERS = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
# Keep-alive settings
KEEP_ALIVE_INTERVAL = 10 # seconds
KEEP_ALIVE_ENABLED = True
# For demo purposes
BATCH_SIZE_EXAMPLE = 3
# ────────────────────────────────────────────────
# SHARED STATE for keep-alive
# ────────────────────────────────────────────────
keep_alive_running = False
keep_alive_thread = None
last_request_time = 0.0
def keep_alive_ping():
"""Minimal request to keep resources warm"""
payload = {
"model": MODEL,
"messages": [{"role": "user", "content": "ping"}],
"max_tokens": 1,
"temperature": 0.0,
"stream": True
}
try:
start = time.time()
r = requests.post(API_URL, headers=HEADERS, json=payload, timeout=15)
elapsed = time.time() - start
if r.status_code == 200:
print(f"[KEEP-ALIVE] OK {elapsed:5.2f}s")
else:
print(f"[KEEP-ALIVE] FAIL {r.status_code} {elapsed:5.2f}s")
except Exception as e:
print(f"[KEEP-ALIVE] ERROR {e}")
def keep_alive_loop():
global last_request_time
while keep_alive_running:
now = time.time()
if now - last_request_time >= KEEP_ALIVE_INTERVAL:
keep_alive_ping()
last_request_time = now # reset after successful ping
time.sleep(3) # check frequently but don't spam
def start_keep_alive():
global keep_alive_running, keep_alive_thread, last_request_time
if not KEEP_ALIVE_ENABLED:
return
if keep_alive_thread is None or not keep_alive_thread.is_alive():
keep_alive_running = True
last_request_time = time.time() - KEEP_ALIVE_INTERVAL - 5 # force first ping soon
keep_alive_thread = threading.Thread(
target=keep_alive_loop,
daemon=True,
name="KeepAlive"
)
keep_alive_thread.start()
print("[KEEP-ALIVE] Background thread started")
def stop_keep_alive():
global keep_alive_running
keep_alive_running = False
# ────────────────────────────────────────────────
# CORE API CALL FUNCTIONS
# ────────────────────────────────────────────────
def call_api_stream(messages: List[Dict[str, str]], extra_params: Dict = None) -> None:
"""Streaming call - prints tokens as they arrive"""
payload = {
"model": MODEL,
"messages": messages,
"temperature": 0.7,
"max_tokens": 300,
"stream": True,
**(extra_params or {})
}
start = time.time()
try:
with requests.post(API_URL, headers=HEADERS, json=payload, stream=True, timeout=60) as r:
r.raise_for_status()
print("[STREAM] Time-to-first-token:")
ttft = None
tokens = 0
content = ""
for line in r.iter_lines():
if line:
line = line.decode('utf-8')
if line.startswith("data: "):
data = line[6:]
if data == "[DONE]":
break
try:
chunk = json.loads(data)
delta = chunk["choices"][0]["delta"]
if "content" in delta and delta["content"]:
token = delta["content"]
print(token, end="", flush=True)
content += token
tokens += 1
if ttft is None:
ttft = time.time() - start
except:
pass
total_time = time.time() - start
print(f"\n[STREAM] Done | TTFT: {ttft:.2f}s | Total: {total_time:.2f}s | {tokens} tokens")
except Exception as e:
print(f"[STREAM] Error: {e}")
def call_api_nonstream(messages: List[Dict[str, str]], extra_params: Dict = None) -> Dict:
"""Non-streaming (full response at once)"""
payload = {
"model": MODEL,
"messages": messages,
"temperature": 0.7,
"max_tokens": 300,
"stream": False,
**(extra_params or {})
}
start = time.time()
try:
r = requests.post(API_URL, headers=HEADERS, json=payload, timeout=60)
r.raise_for_status()
data = r.json()
content = data["choices"][0]["message"]["content"]
total_time = time.time() - start
print(f"[NON-STREAM] {total_time:.2f}s | {len(content.split())} tokens")
return data
except Exception as e:
print(f"[NON-STREAM] Error: {e}")
return {}
def batch_call(prompts: List[str]) -> None:
"""Simple client-side batching demo: send multiple independent questions together"""
if not prompts:
return
messages = [{"role": "system", "content": "Answer each question briefly on a new line starting with Q{i+1}: "}]
for i, p in enumerate(prompts, 1):
messages.append({"role": "user", "content": f"Q{i}: {p}"})
print(f"\n[BATCH] Sending {len(prompts)} questions together...")
call_api_stream(messages)
# ────────────────────────────────────────────────
# DEMO / MAIN
# ────────────────────────────────────────────────
def main():
print("═" * 60)
print(" Latency Optimization Strategies Demo")
print(" Streaming + Batching + Keep-alive")
print("═" * 60)
if "your-api-key-here" in API_KEY:
print("!!! Please set a real SAMBANOVA_API_KEY environment variable !!!\n")
return
start_keep_alive()
# ── Single streaming call ─────────────────────────────────────
print("\n1. Single streaming request")
call_api_stream([
{"role": "user", "content": "Explain in one paragraph why streaming is very helpful for perceived latency."}
])
# ── Non-streaming for comparison ──────────────────────────────
print("\n2. Same request – non-streaming")
call_api_nonstream([
{"role": "user", "content": "Explain in one paragraph why streaming is very helpful for perceived latency."}
])
# ── Batching example ──────────────────────────────────────────
print("\n3. Batching 3 small independent questions")
small_questions = [
"What is the capital of Japan?",
"How many planets in our solar system?",
"What does HTTP stand for?"
]
batch_call(small_questions)
# Let keep-alive run for a bit (you can interrupt with Ctrl+C)
print("\nKeep-alive is running in background. Press Ctrl+C to exit...\n")
try:
while True:
time.sleep(10)
print(f"[STATUS] Running | last activity: {time.time() - last_request_time:.0f}s ago")
except KeyboardInterrupt:
print("\nShutting down...")
stop_keep_alive()
if __name__ == "__main__":
main()
**
Notes**:
-
Use only when latency consistency is critical (interactive chat, agents, real-time apps).
-
Monitor costs — though minimal, high-frequency pings add up.
-
Alternatives: Application-level heartbeat (e.g., periodic dummy query during active sessions), or coordinate keep-alive across users if proxying.
Apply them based on your workload: interactive apps → prioritize streaming + keep-alive; batch/offline → focus on batching.
2 Likes
Thank you very much for such a detailed and helpful explanation.
I now understand that the latency overhead is primarily due to infrastructure scheduling and cold starts in a shared environment.
As our project is a real-time online application, I will implement the stream mode to fetch and process the results. This approach will allow us to handle the data flow more efficiently, ensuring high performance and real-time responsiveness for our system.
The optimization strategies and the Python script you shared are very valuable for our implementation. Thanks again for your support!
3 Likes
