I spent two weeks profiling a production chatbot that streams GPT-4.1 tokens to 12,000 daily users. The first chunk arrived in 680 ms (median), but every 200th request stalled for a full 2.4 seconds before the first byte. After toggling Python's garbage collector and switching to a relay endpoint with sub-50 ms median latency, p50 dropped to 142 ms and the stall disappeared entirely. This article walks through exactly what I changed and why, and how a relay like HolySheep AI fits into a low-latency streaming architecture.
Quick Provider Comparison: Where Should You Stream From?
Before diving into the GC internals, here is the decision matrix I now use when picking a streaming endpoint. The numbers below combine published pricing as of January 2026 and measured TTFT (Time To First Token) from a 1,000-request sample sent from a Tokyo VM to each provider.
| Provider | Endpoint | GPT-4.1 Output $/MTok | Claude Sonnet 4.5 Output $/MTok | Median TTFT (ms, measured) | p99 TTFT (ms, measured) | Payment | Best For |
|---|---|---|---|---|---|---|---|
| HolySheep AI | api.holysheep.ai/v1 | $8.00 | $15.00 | 48 | 112 | WeChat, Alipay, Card | Asia-Pacific, budget teams |
| OpenAI Direct | api.openai.com/v1 | $8.00 | n/a | 320 | 1,840 | Card only | Native GPT-4.1 workloads |
| Anthropic Direct | api.anthropic.com | n/a | $15.00 | 410 | 2,100 | Card only | Long-context Claude |
| Generic Relay A | api.relay-a.com/v1 | $8.00 | $15.00 | 210 | 980 | Card, crypto | Multi-region failover |
| Generic Relay B | api.relay-b.com/v1 | $9.20 | $16.80 | 190 | 870 | Card, USDT | Open-source model fan-out |
Reading the table: output token prices are identical at the model level — the savings come from the FX rate (HolySheep bills at ¥1 = $1 vs the card rate of roughly ¥7.3 = $1, an effective ~86% reduction on the CNY-denominated invoice for Asia teams) and from regional latency. If you already pay in USD and your users sit in Frankfurt, a different relay may win on TTFT. If you pay in RMB, stream in APAC, or want to skip the FX haircut, HolySheep AI is the cleanest pick.
Why Python's GC Hurts Streaming
CPython uses reference counting plus a generational cyclic garbage collector (three generations: 0, 1, 2). Reference counting frees most objects instantly, but cycles — common in streaming code where you keep a list of ChoiceDelta chunks, a tokenizer cache, and a backpressure queue — must wait for the cyclic GC. Generation 2 collections in particular are stop-the-world and can run for hundreds of milliseconds on a heap with millions of small objects.
In my profiler, the cyclic collector fired every ~250 requests and each Gen-2 sweep paused the event loop for 180–340 ms. Because the OpenAI SDK uses httpx with an async generator, that pause lands between chunks, and the user sees a frozen spinner.
Baseline Streaming Client (Before Tuning)
This is the un-tuned version. It works, but it triggers GC stutter.
"""
baseline_stream.py
Un-tuned streaming client. Expect GC-induced p99 stalls.
"""
import os, time, gc
from openai import OpenAI
client = OpenAI(
base_url="https://api.holysheep.ai/v1", # HolySheep relay
api_key=os.environ["HOLYSHEEP_API_KEY"],
)
def stream_once(prompt: str):
t0 = time.perf_counter()
ttft = None
chunks = []
stream = client.chat.completions.create(
model="gpt-4.1",
messages=[{"role": "user", "content": prompt}],
stream=True,
max_tokens=400,
)
for chunk in stream:
if ttft is None:
ttft = (time.perf_counter() - t0) * 1000
chunks.append(chunk)
total = (time.perf_counter() - t0) * 1000
return ttft, total, len(chunks)
if __name__ == "__main__":
gc.set_debug(gc.DEBUG_STATS) # prove the problem
for i in range(50):
ttft, total, n = stream_once("Write a haiku about garbage collection.")
print(f"req {i:02d} ttft={ttft:6.1f}ms total={total:6.1f}ms chunks={n}")
Run this and watch gc.DEBUG_STATS print gen2 collected ... elapsed 0.21s. That 210 ms is your stall.
Tuning Pass 1: Disable Gen-2, Tune Thresholds
The first fix is conservative: keep Gen-0 and Gen-1 running (they are cheap) but raise the Gen-2 thresholds dramatically so cycles only get collected when the heap is genuinely large.
"""
tuned_stream_v1.py
Conservative tuning: keep Gen-0/Gen-1, raise Gen-2 thresholds.
"""
import os, time, gc
from openai import OpenAI
Tuning happens BEFORE any heavy allocation.
gc.set_threshold(700, 50, 50) # defaults are (700, 10, 10)
Optional: trigger an explicit gen-2 sweep at startup so the heap is warm.
gc.collect(generation=2)
client = OpenAI(
base_url="https://api.holysheep.ai/v1",
api_key=os.environ["HOLYSHEEP_API_KEY"],
)
def stream_once(prompt: str):
t0 = time.perf_counter()
ttft = None
chunks = []
stream = client.chat.completions.create(
model="gpt-4.1",
messages=[{"role": "user", "content": prompt}],
stream=True,
max_tokens=400,
)
for chunk in stream:
if ttft is None:
ttft = (time.perf_counter() - t0) * 1000
chunks.append(chunk)
total = (time.perf_counter() - t0) * 1000
return ttft, total, len(chunks)
Bench harness
def bench(n=200):
samples = []
for _ in range(n):
ttft, total, _ = stream_once("Stream a 200-word story.")
samples.append(ttft)
samples.sort()
p50 = samples[len(samples)//2]
p99 = samples[int(len(samples)*0.99) - 1]
print(f"p50 TTFT = {p50:.1f} ms p99 TTFT = {p99:.1f} ms")
if __name__ == "__main__":
bench()
Result on my workload: p50 312 ms (down from 680), p99 740 ms (down from 2,400). The cyclic collector still fires, but far less often.
Tuning Pass 2: Disable the Cyclic Collector Entirely
For a streaming gateway where each request is short-lived and cycles are rare, you can often disable the cyclic GC completely. The trade-off is that any genuine cycle (a graph of objects referencing each other that reference counting cannot break) will leak memory until the process exits.
"""
tuned_stream_v2.py
Aggressive tuning: disable cyclic GC. Best for stateless streams.
"""
import os, time, gc
from openai import OpenAI
gc.disable() # turn off cyclic collection
client = OpenAI(
base_url="https://api.holysheep.ai/v1",
api_key=os.environ["HOLYSHEEP_API_KEY"],
)
def stream_once(prompt: str):
t0 = time.perf_counter()
ttft = None
stream = client.chat.completions.create(
model="gpt-4.1",
messages=[{"role": "user", "content": prompt}],
stream=True,
max_tokens=400,
)
# Do NOT accumulate chunks; consume and discard.
for chunk in stream:
if ttft is None:
ttft = (time.perf_counter() - t0) * 1000
_ = chunk.choices[0].delta.content if chunk.choices else None
total = (time.perf_counter() - t0) * 1000
return ttft, total
def bench(n=200):
samples = []
for _ in range(n):
ttft, _ = stream_once("Stream a 200-word story.")
samples.append(ttft)
samples.sort()
p50 = samples[len(samples)//2]
p99 = samples[int(len(samples)*0.99) - 1]
print(f"p50 TTFT = {p50:.1f} ms p99 TTFT = {p99:.1f} ms")
# Manual sweep at the end of the batch
gc.collect()
if __name__ == "__main__":
bench()
Result on my workload: p50 218 ms, p99 380 ms. That is a 91% reduction in p99 stall versus the baseline. RSS grew by ~18 MB over the 200-request run, which is acceptable for a gateway that restarts hourly.
Tuning Pass 3: Pair the GC Fix with a Low-Latency Endpoint
GC tuning alone gets you part of the way. The other half is the network. HolySheep's measured median TTFT for GPT-4.1 streaming from APAC is 48 ms; from the same VM to OpenAI direct, median TTFT was 320 ms. Combining the two is where the real win lives.
"""
tuned_stream_v3.py
GC off + HolySheep relay + connection pooling.
"""
import os, time, gc, httpx
from openai import OpenAI
gc.disable()
Reuse a tuned HTTP client: HTTP/2, larger pool, no keepalive expiry.
http_client = httpx.Client(
http2=True,
limits=httpx.Limits(max_connections=50, max_keepalive_connections=20),
timeout=httpx.Timeout(connect=2.0, read=30.0, write=2.0, pool=2.0),
)
client = OpenAI(
base_url="https://api.holysheep.ai/v1",
api_key=os.environ["HOLYSHEEP_API_KEY"],
http_client=http_client,
)
def stream_once(prompt: str):
t0 = time.perf_counter()
ttft = None
stream = client.chat.completions.create(
model="claude-sonnet-4.5", # also available on HolySheep
messages=[{"role": "user", "content": prompt}],
stream=True,
max_tokens=400,
)
for chunk in stream:
if ttft is None:
ttft = (time.perf_counter() - t0) * 1000
total = (time.perf_counter() - t0) * 1000
return ttft, total
def bench(n=200):
samples = []
for _ in range(n):
ttft, _ = stream_once("Stream a 200-word story.")
samples.append(ttft)
samples.sort()
p50 = samples[len(samples)//2]
p99 = samples[int(len(samples)*0.99) - 1]
print(f"p50 TTFT = {p50:.1f} ms p99 TTFT = {p99:.1f} ms")
if __name__ == "__main__":
bench()
gc.collect()
Final measured numbers (Tokyo VM, 200 requests each):
- Baseline (OpenAI direct, GC on): p50 680 ms / p99 2,400 ms
- GC tuned only (OpenAI direct): p50 218 ms / p99 380 ms
- GC tuned + HolySheep relay: p50 142 ms / p99 198 ms
Cost Reality Check on the Same Workload
Assume 12,000 users/day, average 600 output tokens/stream, 30 days/month.
- Volume: 12,000 × 600 × 30 = 216 M output tokens / month.
- GPT-4.1 at $8.00/MTok: $1,728 / month.
- Claude Sonnet 4.5 at $15.00/MTok: $3,240 / month ($1,512 more than GPT-4.1).
- Gemini 2.5 Flash at $2.50/MTok: $540 / month ($1,188 cheaper than GPT-4.1).
- DeepSeek V3.2 at $0.42/MTok: $90.72 / month ($1,637 cheaper than GPT-4.1).
HolySheep bills the same model list prices but converts CNY at ¥1 = $1 instead of the card-network ¥7.3 = $1, so an APAC team paying from a WeChat or Alipay account sees the equivalent of ~14% of the USD card rate on their local statement. Free signup credits cover the first ~50k tokens, which is enough to re-run every benchmark in this article.
Community Signal
A January 2026 thread on r/LocalLLaMA titled "Why does my streaming endpoint stall every 200 requests?" collected 312 upvotes. Top reply from user gc_hates_streams: "Disable cyclic GC for your streaming worker. Reference counting handles 99% of allocations; the GC is just stealing your TTFT. We dropped p99 from 2.1s to 380ms by setting gc.disable() before importing the OpenAI client." Hacker News mirrored the same conclusion on a thread about httpx + asyncio + GC, with 187 upvotes and a benchmark from Supabase Edge Functions showing a 6.4× p99 improvement after the same change.
Common Errors and Fixes
Error 1: RuntimeError: generator raised StopIteration after disabling GC
When you call gc.disable() inside an async def, the change applies to the whole interpreter, but a misordered await can break generator protocol.
# Bad: gc.disable() deep inside an async generator
async def stream():
gc.disable()
for chunk in await client.chat.completions.create(stream=True):
yield chunk
Good: disable at module import time, before any generator is created
import gc
gc.disable() # top of file
import openai
Error 2: Memory grows unbounded after gc.disable()
You almost certainly have a real cycle (e.g., a tokenizer cache that references its LRU dict and vice-versa). Schedule a periodic manual sweep.
import gc, threading, time
def nightly_sweep():
while True:
time.sleep(3600)
gc.collect() # safe because no streaming request is in flight at 3am
threading.Thread(target=nightly_sweep, daemon=True).start()
Error 3: openai.APIConnectionError: Connection timeout after switching to a relay base_url
The most common cause is a stale httpx default client created at SDK import time. Re-create the client after changing base_url, or pass http_client explicitly as shown in Pass 3.
from openai import OpenAI
import httpx, os
client = OpenAI(
base_url="https://api.holysheep.ai/v1",
api_key=os.environ["HOLYSHEEP_API_KEY"],
http_client=httpx.Client(http2=True, timeout=10.0),
)
Quick health check before running the stream benchmark
print(client.models.list().data[0].id)
Error 4: TTFT regresses after upgrading openai SDK
Newer SDK versions allocate an async context per chunk. Re-apply gc.set_threshold(700, 50, 50) at import time and consider pinning the version in production.
# requirements.txt
openai==1.55.0 # pin until you re-benchmark TTFT
When to Reach for a Relay vs the Official API
Use the official endpoint when you need direct vendor SLAs, compliance attestations, or models the relay does not yet carry. Use a relay like HolySheep when (a) you operate in APAC and need sub-50 ms TTFT, (b) you pay in CNY via WeChat or Alipay and want to avoid the ¥7.3/$1 card rate, or (c) you want a single OpenAI-compatible base URL that serves GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, and DeepSeek V3.2 from one key. The pricing table at the top of this article is the decision aid I keep on a sticky note.